diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..ac978988309dfe0988394ff66881e85e606e41c4 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,27 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Down/graphics/graphics/down_paradox_tabular_reward.png filter=lfs diff=lfs merge=lfs -text
+Down/graphics/graphics/dual_down_antidown_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/AntiStrange/graphics/antistrange_diversity.png filter=lfs diff=lfs merge=lfs -text
+Strange/AntiStrange/graphics/antistrange_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/AntiStrange/graphics/antistrange_phase.png filter=lfs diff=lfs merge=lfs -text
+Strange/AntiStrange/graphics/antistrange_progress.png filter=lfs diff=lfs merge=lfs -text
+Strange/AntiStrange/graphics/antistrange_stability.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/antistrange_diversity.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/antistrange_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/antistrange_phase.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/antistrange_progress.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/antistrange_stability.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/dual_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/dual_stability.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/strange_diversity.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/strange_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/strange_phase.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/strange_progress.png filter=lfs diff=lfs merge=lfs -text
+Strange/graphics/graphics/strange_stability.png filter=lfs diff=lfs merge=lfs -text
+Strange/strange/graphics/strange_diversity.png filter=lfs diff=lfs merge=lfs -text
+Strange/strange/graphics/strange_episode_return.png filter=lfs diff=lfs merge=lfs -text
+Strange/strange/graphics/strange_phase.png filter=lfs diff=lfs merge=lfs -text
+Strange/strange/graphics/strange_progress.png filter=lfs diff=lfs merge=lfs -text
+Strange/strange/graphics/strange_stability.png filter=lfs diff=lfs merge=lfs -text
diff --git a/Down/AntiDown/envs/corrupted_valley.py b/Down/AntiDown/envs/corrupted_valley.py
new file mode 100644
index 0000000000000000000000000000000000000000..52649de040c3cec0a58a9bd8eaf44c3f199aad41
--- /dev/null
+++ b/Down/AntiDown/envs/corrupted_valley.py
@@ -0,0 +1,132 @@
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Dict, Any
+
+import numpy as np
+
+
+@dataclass
+class StepResult:
+    obs: int
+    reward: float
+    done: bool
+    info: Dict[str, Any]
+
+
+class CorruptedValleyEnv:
+    """
+    7x7 gridworld with:
+      - Start S at bottom-left (row=6, col=0)
+      - Goal G at top-right (row=0, col=6)
+      - Corrupted valley V in the center of the grid.
+
+    Phases:
+        1: Valley is falsely attractive (positive bonus).
+        2: Valley is corrected (neutral or mildly negative).
+        3: Same rewards as phase 2. Intended for noisy-training experiments.
+
+    Observation is a single integer state index in [0, n_states).
+    Actions: 0=up, 1=right, 2=down, 3=left.
+    """
+
+    def __init__(
+        self,
+        seed: Optional[int] = None,
+        grid_size: int = 7,
+        valley_reward_phase1: float = 2.0,
+        valley_reward_phase2: float = -0.5,
+        step_penalty: float = -0.01,
+        max_steps: int = 50,
+        phase: int = 1,
+    ) -> None:
+        self.grid_size = grid_size
+        self.n_states = grid_size * grid_size
+        self.n_actions = 4
+
+        self.valley_reward_phase1 = valley_reward_phase1
+        self.valley_reward_phase2 = valley_reward_phase2
+        self.step_penalty = step_penalty
+        self.max_steps = max_steps
+
+        self.start_pos = (grid_size - 1, 0)
+        self.goal_pos = (0, grid_size - 1)
+
+        self.rng = np.random.default_rng(seed)
+        self.phase = phase
+
+        # center 3x3 block as valley
+        c = grid_size // 2
+        self.valley_cells = {
+            (r, c2)
+            for r in range(c - 1, c + 2)
+            for c2 in range(c - 1, c + 2)
+        }
+
+        self.pos: Tuple[int, int] = self.start_pos
+        self.steps = 0
+
+    def _state_from_pos(self, pos: Tuple[int, int]) -> int:
+        r, c = pos
+        return r * self.grid_size + c
+
+    def _obs(self) -> int:
+        return self._state_from_pos(self.pos)
+
+    def reset(self) -> int:
+        self.pos = self.start_pos
+        self.steps = 0
+        return self._obs()
+
+    def set_phase(self, phase: int) -> None:
+        if phase not in (1, 2, 3):
+            raise ValueError(f"Invalid phase: {phase}")
+        self.phase = phase
+
+    def step(self, action: int) -> StepResult:
+        self.steps += 1
+
+        r, c = self.pos
+        if action == 0:      # up
+            r -= 1
+        elif action == 1:    # right
+            c += 1
+        elif action == 2:    # down
+            r += 1
+        elif action == 3:    # left
+            c -= 1
+
+        # clamp to grid
+        r = max(0, min(self.grid_size - 1, r))
+        c = max(0, min(self.grid_size - 1, c))
+        self.pos = (r, c)
+
+        reward = self.step_penalty
+        done = False
+        info: Dict[str, Any] = {}
+
+        in_valley = self.pos in self.valley_cells
+        info["valley"] = in_valley
+
+        if self.pos == self.goal_pos:
+            reward += 1.0
+            done = True
+            info["terminal"] = "goal"
+        elif in_valley:
+            if self.phase == 1:
+                reward += self.valley_reward_phase1
+            else:
+                reward += self.valley_reward_phase2
+
+        if self.steps >= self.max_steps and not done:
+            done = True
+            info["terminal"] = "timeout"
+
+        return StepResult(
+            obs=self._obs(),
+            reward=reward,
+            done=done,
+            info=info,
+        )
+
+    def sample_action(self) -> int:
+        return self.rng.integers(0, self.n_actions)
diff --git a/Down/AntiDown/exp/plot_corrupted_valley_results.py b/Down/AntiDown/exp/plot_corrupted_valley_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..a53839fb26c21ab091c09d3cb0188f1c8b8e1198
--- /dev/null
+++ b/Down/AntiDown/exp/plot_corrupted_valley_results.py
@@ -0,0 +1,135 @@
+"""
+Plot results for the Corrupted Valley tabular experiment.
+
+Expects a CSV file created by `exp/run_corrupted_valley_tabular.py` with columns:
+    phase, phase_tag, episode, agent, total_reward, valley_visits
+"""
+
+# --- twoquarks bootstrap (path-stable imports) ---
+import sys
+from pathlib import Path
+_ROOT = Path(__file__).resolve().parents[1]  # project root (sibling of exp/)
+if str(_ROOT) not in sys.path:
+    sys.path.insert(0, str(_ROOT))
+# -------------------------------------------------
+
+
+import csv
+import os
+from collections import defaultdict
+from typing import Dict, List, Tuple
+
+import matplotlib.pyplot as plt
+
+
+RESULTS_DIR = Path(os.environ.get("TWOQUARKS_RESULTS_DIR", (Path(__file__).resolve().parent.parent / "results").as_posix()))
+RESULTS_CSV = str(RESULTS_DIR / "antidown_corrupted_valley_tabular.csv")
+OUT_DIR = Path(os.environ.get("TWOQUARKS_GRAPHICS_DIR", (Path(__file__).resolve().parent.parent / "graphics").as_posix()))
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+OUT_PREFIX = str(OUT_DIR / "antidown_corrupted_valley_")
+
+
+def load_results(path: str) -> List[Dict[str, str]]:
+    rows: List[Dict[str, str]] = []
+    with open(path, mode="r", newline="") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            rows.append(row)
+    return rows
+
+
+def build_global_index(rows: List[Dict[str, str]]) -> Tuple[Dict[int, int], Dict[int, Dict[str, List[Tuple[int, float, float]]]]]:
+    """
+    Returns:
+        phase_to_max_ep: phase -> max episode index
+        data: phase -> agent -> list of (global_ep, total_reward, valley_visits)
+    """
+    phase_to_eps: Dict[int, List[int]] = defaultdict(list)
+    for r in rows:
+        phase = int(r["phase"])
+        ep = int(r["episode"])
+        phase_to_eps[phase].append(ep)
+
+    phase_to_max_ep: Dict[int, int] = {
+        phase: max(eps) + 1 for phase, eps in phase_to_eps.items()
+    }
+
+    data: Dict[int, Dict[str, List[Tuple[int, float, float]]]] = defaultdict(
+        lambda: defaultdict(list)
+    )
+
+    for r in rows:
+        phase = int(r["phase"])
+        agent = r["agent"]
+        ep = int(r["episode"])
+        total_reward = float(r["total_reward"])
+        valley_visits = float(r["valley_visits"])
+        max_ep = phase_to_max_ep[phase]
+        global_ep = (phase - 1) * max_ep + ep
+        data[phase][agent].append((global_ep, total_reward, valley_visits))
+
+    return phase_to_max_ep, data
+
+
+def plot_metric(
+    data: Dict[int, Dict[str, List[Tuple[int, float, float]]]],
+    metric_index: int,
+    ylabel: str,
+    out_path: str,
+) -> None:
+    """
+    metric_index: 1 for total_reward, 2 for valley_visits.
+    """
+    plt.figure()
+
+    # merge across phases, keeping breaks in the global index
+    agent_to_xy: Dict[str, Tuple[List[int], List[float]]] = {}
+
+    for phase, agents in sorted(data.items()):
+        for agent, triples in agents.items():
+            xs = [t[0] for t in triples]
+            ys = [t[metric_index] for t in triples]
+            if agent not in agent_to_xy:
+                agent_to_xy[agent] = ([], [])
+            agent_to_xy[agent][0].extend(xs)
+            agent_to_xy[agent][1].extend(ys)
+
+    for agent, (xs, ys) in agent_to_xy.items():
+        # sort by x
+        pairs = sorted(zip(xs, ys), key=lambda p: p[0])
+        xs_sorted = [p[0] for p in pairs]
+        ys_sorted = [p[1] for p in pairs]
+        plt.plot(xs_sorted, ys_sorted, label=agent)
+
+    plt.xlabel("global episode index")
+    plt.ylabel(ylabel)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(out_path)
+    plt.close()
+
+
+def main() -> None:
+    if not os.path.exists(RESULTS_CSV):
+        raise FileNotFoundError(f"Results file not found: {RESULTS_CSV}")
+
+    rows = load_results(RESULTS_CSV)
+    _, data = build_global_index(rows)
+
+    plot_metric(
+        data,
+        metric_index=1,
+        ylabel="total reward per episode",
+        out_path=OUT_PREFIX + "reward.png",
+    )
+
+    plot_metric(
+        data,
+        metric_index=2,
+        ylabel="valley visits per episode",
+        out_path=OUT_PREFIX + "valley_visits.png",
+    )
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/Down/AntiDown/exp/run_corrupted_valley_tabular.py b/Down/AntiDown/exp/run_corrupted_valley_tabular.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e22fde3261c5cc94c219bc8cd35a26a30f819a3
--- /dev/null
+++ b/Down/AntiDown/exp/run_corrupted_valley_tabular.py
@@ -0,0 +1,304 @@
+"""
+Tabular experiments in the Corrupted Valley Gridworld.
+
+Agents:
+- EpsGreedyQAgent (baseline)
+- SoftmaxBoltzmannAgent
+- LevoQTabularAgent
+- LevoThinkingEnsembleAgent
+
+Phases:
+1) Corrupted reward in the valley (falsely attractive).
+2) Corrected reward.
+3) Corrected reward + noisy TD-updates for LevoThinking heads.
+"""
+
+# --- twoquarks bootstrap (path-stable imports) ---
+import sys
+from pathlib import Path
+_ROOT = Path(__file__).resolve().parents[1]  # project root (sibling of exp/)
+if str(_ROOT) not in sys.path:
+    sys.path.insert(0, str(_ROOT))
+# -------------------------------------------------
+
+
+import os
+from typing import Dict, List, Optional, Iterable
+
+import numpy as np
+
+from envs.corrupted_valley import CorruptedValleyEnv
+from levo.levo_q_tabular import LevoQTabularAgent
+from levo.levo_thinking_ensemble import LevoThinkingEnsembleAgent
+from utils.logging import CSVLogger
+
+
+class EpsGreedyQAgent:
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        epsilon: float = 0.1,
+        gamma: float = 0.99,
+        alpha: float = 0.1,
+        name: str = "EpsGreedy",
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.epsilon = epsilon
+        self.gamma = gamma
+        self.alpha = alpha
+        self.name = name
+        self.Q = np.zeros((n_states, n_actions), dtype=float)
+
+    def select_action(self, state: int, rng: np.random.Generator) -> int:
+        if rng.random() < self.epsilon:
+            return int(rng.integers(0, self.n_actions))
+        q_s = self.Q[state]
+        return int(rng.choice(np.flatnonzero(q_s == q_s.max())))
+
+    def update(self, s: int, a: int, r: float, s_next: int, done: bool) -> None:
+        q_sa = self.Q[s, a]
+        if done:
+            target = r
+        else:
+            target = r + self.gamma * float(self.Q[s_next].max())
+        td = target - q_sa
+        self.Q[s, a] = q_sa + self.alpha * td
+
+
+class SoftmaxBoltzmannAgent:
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        tau: float = 0.5,
+        gamma: float = 0.99,
+        alpha: float = 0.1,
+        name: str = "Softmax",
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.tau = tau
+        self.gamma = gamma
+        self.alpha = alpha
+        self.name = name
+        self.Q = np.zeros((n_states, n_actions), dtype=float)
+
+    def _softmax(self, q_s: np.ndarray) -> np.ndarray:
+        q = q_s / max(self.tau, 1e-8)
+        q = q - np.max(q)
+        ex = np.exp(q)
+        s = ex.sum()
+        if s <= 0.0:
+            return np.ones_like(ex) / len(ex)
+        return ex / s
+
+    def select_action(self, state: int, rng: np.random.Generator) -> int:
+        probs = self._softmax(self.Q[state])
+        return int(rng.choice(self.n_actions, p=probs))
+
+    def update(self, s: int, a: int, r: float, s_next: int, done: bool) -> None:
+        q_sa = self.Q[s, a]
+        if done:
+            target = r
+        else:
+            target = r + self.gamma * float(self.Q[s_next].max())
+        td = target - q_sa
+        self.Q[s, a] = q_sa + self.alpha * td
+
+
+def run_episode(
+    env: CorruptedValleyEnv,
+    agent,
+    phase_id: int,
+    episode_idx: int,
+    logger: CSVLogger,
+    rng: np.random.Generator,
+    noisy_heads: Optional[Iterable[int]] = None,
+    noise_std: float = 0.0,
+) -> None:
+    obs = env.reset()
+    total_reward = 0.0
+    valley_visits = 0
+    done = False
+
+    while not done:
+        action = agent.select_action(obs, rng=rng)
+        step = env.step(action)
+        next_obs = step.obs
+        reward = step.reward
+        done = step.done
+        info = step.info
+
+        total_reward += reward
+        if info.get("valley", False):
+            valley_visits += 1
+
+        if isinstance(agent, LevoThinkingEnsembleAgent):
+            agent.update(
+                obs,
+                action,
+                reward,
+                next_obs,
+                done,
+                noisy_heads=noisy_heads,
+                noise_std=noise_std,
+                rng=rng,
+            )
+        else:
+            agent.update(obs, action, reward, next_obs, done)
+
+        obs = next_obs
+
+    logger.log(
+        {
+            "phase": phase_id,
+            "phase_tag": f"phase{phase_id}",
+            "episode": episode_idx,
+            "agent": agent.name,
+            "total_reward": total_reward,
+            "valley_visits": valley_visits,
+        }
+    )
+
+
+def run_phase(
+    env_seed_base: int,
+    env_seed_offset: int,
+    phase_id: int,
+    n_episodes: int,
+    agents: List,
+    logger: CSVLogger,
+    noisy_heads_spec: Optional[Iterable[int]] = None,
+    noise_std: float = 0.0,
+) -> None:
+    """
+    Run one phase for all agents.
+
+    Seeds are constructed as:
+        env_seed = env_seed_base + phase_id * env_seed_offset + agent_idx * 10000 + episode_idx
+    so that episodes are reproducible but independent across agents and phases.
+    """
+    for agent_idx, agent in enumerate(agents):
+        for ep in range(n_episodes):
+            env_seed = (
+                env_seed_base
+                + phase_id * env_seed_offset
+                + agent_idx * 10000
+                + ep
+            )
+            env = CorruptedValleyEnv(seed=env_seed, phase=phase_id)
+            rng = np.random.default_rng(env_seed)
+
+            if isinstance(agent, LevoThinkingEnsembleAgent):
+                if noisy_heads_spec is None:
+                    # default: half of the heads receive noisy TD-updates
+                    k = max(1, agent.n_heads // 2)
+                    effective_noisy_heads = range(k)
+                else:
+                    effective_noisy_heads = noisy_heads_spec
+                episode_noise_std = noise_std
+            else:
+                effective_noisy_heads = None
+                episode_noise_std = 0.0
+
+            run_episode(
+                env=env,
+                agent=agent,
+                phase_id=phase_id,
+                episode_idx=ep,
+                logger=logger,
+                rng=rng,
+                noisy_heads=effective_noisy_heads,
+                noise_std=episode_noise_std,
+            )
+
+
+def main() -> None:
+    base_seed = 1234
+    seed_offset = 1000
+
+    # probe environment to get sizes
+    probe_env = CorruptedValleyEnv(seed=base_seed, phase=1)
+    n_states = probe_env.n_states
+    n_actions = probe_env.n_actions
+
+    agents = [
+        EpsGreedyQAgent(n_states, n_actions, epsilon=0.1, name="EpsGreedy"),
+        SoftmaxBoltzmannAgent(n_states, n_actions, tau=0.5, name="Softmax"),
+        LevoQTabularAgent(
+            n_states,
+            n_actions,
+            A=0.5,
+            omega=0.05,
+            ent_weight=0.0,
+            name="LevoQ",
+        ),
+        LevoThinkingEnsembleAgent(
+            n_states,
+            n_actions,
+            n_heads=5,
+            A=0.5,
+            omega=0.05,
+            lambda_var=0.5,
+            name="LevoThinking",
+        ),
+    ]
+
+    _results_dir = Path(os.environ.get("TWOQUARKS_RESULTS_DIR", (Path(__file__).resolve().parents[1] / "results").as_posix()))
+    _results_dir.mkdir(parents=True, exist_ok=True)
+    results_path = str(_results_dir / "antidown_corrupted_valley_tabular.csv")
+    logger = CSVLogger(
+        results_path,
+        fieldnames=[
+            "phase",
+            "phase_tag",
+            "episode",
+            "agent",
+            "total_reward",
+            "valley_visits",
+        ],
+    )
+
+    n_episodes = 200
+
+    # Phase 1: corrupted valley
+    run_phase(
+        env_seed_base=base_seed,
+        env_seed_offset=seed_offset,
+        phase_id=1,
+        n_episodes=n_episodes,
+        agents=agents,
+        logger=logger,
+        noisy_heads_spec=None,
+        noise_std=0.0,
+    )
+
+    # Phase 2: corrected valley
+    run_phase(
+        env_seed_base=base_seed,
+        env_seed_offset=seed_offset,
+        phase_id=2,
+        n_episodes=n_episodes,
+        agents=agents,
+        logger=logger,
+        noisy_heads_spec=None,
+        noise_std=0.0,
+    )
+
+    # Phase 3: corrected valley + noisy TD-updates for LevoThinking
+    run_phase(
+        env_seed_base=base_seed,
+        env_seed_offset=seed_offset,
+        phase_id=3,
+        n_episodes=n_episodes,
+        agents=agents,
+        logger=logger,
+        noisy_heads_spec=None,  # default: half of the heads
+        noise_std=0.1,
+    )
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/Down/AntiDown/levo/levo_q_tabular.py b/Down/AntiDown/levo/levo_q_tabular.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bf91bf1e5211e2dfc6a1c9514cf1826d5847e6f
--- /dev/null
+++ b/Down/AntiDown/levo/levo_q_tabular.py
@@ -0,0 +1,71 @@
+from typing import Optional
+
+import numpy as np
+
+from .policies import hf_levo_policy_tabular
+
+
+class LevoQTabularAgent:
+    """
+    Tabular Q-learning agent with an HF-Levo policy.
+
+    The Q-update is standard:
+        Q(s, a) <- Q(s, a) + alpha * (r + gamma max_a' Q(s', a') - Q(s, a))
+
+    The policy is:
+        score(a) = Q(s, a) + A cos(omega * t + phi_a + phase_offset)
+        pi(a|s)  = softmax(score / tau)
+    """
+
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        gamma: float = 0.99,
+        alpha: float = 0.1,
+        A: float = 0.5,
+        omega: float = 0.1,
+        phase_offset: float = 0.0,
+        ent_weight: float = 0.0,
+        prior: Optional[np.ndarray] = None,
+        tau: float = 1.0,
+        name: str = "LevoQ",
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.gamma = gamma
+        self.alpha = alpha
+        self.A = A
+        self.omega = omega
+        self.phase_offset = phase_offset
+        self.ent_weight = ent_weight
+        self.prior = prior
+        self.tau = tau
+
+        self.name = name
+        self.Q = np.zeros((n_states, n_actions), dtype=float)
+        self.t = 0  # global step counter for the HF modulation
+
+    def select_action(self, state: int, rng: np.random.Generator) -> int:
+        probs = hf_levo_policy_tabular(
+            self.Q,
+            state,
+            t=self.t,
+            A=self.A,
+            omega=self.omega,
+            phase_offset=self.phase_offset,
+            ent_weight=self.ent_weight,
+            prior=self.prior,
+            tau=self.tau,
+        )
+        self.t += 1
+        return int(rng.choice(self.n_actions, p=probs))
+
+    def update(self, s: int, a: int, r: float, s_next: int, done: bool) -> None:
+        q_sa = self.Q[s, a]
+        if done:
+            target = r
+        else:
+            target = r + self.gamma * float(self.Q[s_next].max())
+        td = target - q_sa
+        self.Q[s, a] = q_sa + self.alpha * td
diff --git a/Down/AntiDown/levo/levo_thinking_ensemble.py b/Down/AntiDown/levo/levo_thinking_ensemble.py
new file mode 100644
index 0000000000000000000000000000000000000000..7193fb283bc46443af37942e8462b016b121c2f0
--- /dev/null
+++ b/Down/AntiDown/levo/levo_thinking_ensemble.py
@@ -0,0 +1,104 @@
+from typing import Iterable, Optional
+
+import numpy as np
+
+from .policies import ensemble_levo_thinking_policy
+
+
+class LevoThinkingEnsembleAgent:
+    """
+    Ensemble Q-learning agent with LevoThinking policy.
+
+    Q has shape (n_heads, n_states, n_actions).
+
+    For action selection, we use:
+        mu(a)  = mean_h Q_h(s, a)
+        var(a) = var_h  Q_h(s, a)
+        base_score(a) = mu(a) - lambda_var * var(a)
+        score(a)      = base_score(a) + HF(t, a)
+
+    Each head is updated independently with standard Q-learning, optionally
+    adding Gaussian noise to the TD-error for a subset of heads.
+    """
+
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        n_heads: int = 5,
+        gamma: float = 0.99,
+        alpha: float = 0.1,
+        A: float = 0.5,
+        omega: float = 0.1,
+        phase_offset: float = 0.0,
+        lambda_var: float = 0.5,
+        prior: Optional[np.ndarray] = None,
+        tau: float = 1.0,
+        name: str = "LevoThinking",
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.n_heads = n_heads
+        self.gamma = gamma
+        self.alpha = alpha
+        self.A = A
+        self.omega = omega
+        self.phase_offset = phase_offset
+        self.lambda_var = lambda_var
+        self.prior = prior
+        self.tau = tau
+
+        self.name = name
+        self.Q = np.zeros((n_heads, n_states, n_actions), dtype=float)
+        self.t = 0  # global step counter for the HF modulation
+
+    def select_action(self, state: int, rng: np.random.Generator) -> int:
+        probs = ensemble_levo_thinking_policy(
+            self.Q,
+            state,
+            t=self.t,
+            A=self.A,
+            omega=self.omega,
+            phase_offset=self.phase_offset,
+            lambda_var=self.lambda_var,
+            prior=self.prior,
+            tau=self.tau,
+        )
+        self.t += 1
+        return int(rng.choice(self.n_actions, p=probs))
+
+    def update(
+        self,
+        s: int,
+        a: int,
+        r: float,
+        s_next: int,
+        done: bool,
+        noisy_heads: Optional[Iterable[int]] = None,
+        noise_std: float = 0.0,
+        rng: Optional[np.random.Generator] = None,
+    ) -> None:
+        """
+        Update each head with standard Q-learning.
+
+        If `noisy_heads` is not None and `noise_std > 0`, then for any head h
+        in `noisy_heads`, we add N(0, noise_std) to the TD-error before the
+        update. This is used in Phase 3 to simulate corrupted learning signals.
+        """
+        if noisy_heads is None:
+            noisy_set = set()
+        else:
+            noisy_set = set(int(h) for h in noisy_heads)
+
+        for h in range(self.n_heads):
+            q_sa = self.Q[h, s, a]
+            if done:
+                target = r
+            else:
+                target = r + self.gamma * float(self.Q[h, s_next].max())
+            td = target - q_sa
+
+            if h in noisy_set and noise_std > 0.0 and rng is not None:
+                td += float(rng.normal(loc=0.0, scale=noise_std))
+
+            self.Q[h, s, a] = q_sa + self.alpha * td
diff --git a/Down/AntiDown/levo/policies.py b/Down/AntiDown/levo/policies.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c6d170d995195e4e837e7330ba3cf96849d91f0
--- /dev/null
+++ b/Down/AntiDown/levo/policies.py
@@ -0,0 +1,101 @@
+import math
+from typing import Optional
+
+import numpy as np
+
+
+def softmax(x: np.ndarray, tau: float = 1.0) -> np.ndarray:
+    x = np.asarray(x, dtype=float)
+    if tau <= 0.0:
+        raise ValueError("tau must be positive")
+    x = x / tau
+    x = x - np.max(x)
+    ex = np.exp(x)
+    s = ex.sum()
+    if s <= 0.0:
+        # fallback: uniform
+        return np.ones_like(ex) / len(ex)
+    return ex / s
+
+
+def hf_levo_policy_tabular(
+    Q: np.ndarray,
+    state: int,
+    t: int,
+    A: float = 0.5,
+    omega: float = 0.1,
+    phase_offset: float = 0.0,
+    ent_weight: float = 0.0,
+    prior: Optional[np.ndarray] = None,
+    tau: float = 1.0,
+) -> np.ndarray:
+    """
+    HF-Levo policy on a single Q-table.
+
+    score(a) = Q(s, a) + A cos(omega * t + phi_a + phase_offset)
+    """
+    q_s = np.asarray(Q[state], dtype=float)
+    n_actions = q_s.shape[0]
+
+    phases = np.linspace(0.0, 2.0 * math.pi, n_actions, endpoint=False)
+    osc = A * np.cos(omega * float(t) + phases + phase_offset)
+    score = q_s + osc
+
+    p = softmax(score, tau=tau)
+
+    if prior is None or ent_weight <= 0.0:
+        return p
+
+    prior = np.asarray(prior, dtype=float)
+    prior = prior / max(prior.sum(), 1e-8)
+    w = max(0.0, min(1.0, float(ent_weight)))
+    p_mix = (1.0 - w) * p + w * prior
+    p_mix = np.clip(p_mix, 1e-8, 1.0)
+    p_mix /= p_mix.sum()
+    return p_mix
+
+
+def ensemble_levo_thinking_policy(
+    Q_ensemble: np.ndarray,
+    state: int,
+    t: int,
+    A: float = 0.5,
+    omega: float = 0.1,
+    phase_offset: float = 0.0,
+    lambda_var: float = 0.5,
+    prior: Optional[np.ndarray] = None,
+    tau: float = 1.0,
+) -> np.ndarray:
+    """
+    LevoThinking ensemble policy.
+
+    Let Q_h be the Q-table of head h.
+
+    mu(a)  = mean_h Q_h(s, a)
+    var(a) = var_h  Q_h(s, a)
+
+    base_score(a) = mu(a) - lambda_var * var(a)
+    score(a)      = base_score(a) + A cos(omega * t + phi_a + phase_offset)
+    """
+    q_heads = np.asarray(Q_ensemble[:, state, :], dtype=float)
+    mu = q_heads.mean(axis=0)
+    var = q_heads.var(axis=0)
+
+    n_actions = mu.shape[0]
+    phases = np.linspace(0.0, 2.0 * math.pi, n_actions, endpoint=False)
+    osc = A * np.cos(omega * float(t) + phases + phase_offset)
+
+    base_score = mu - lambda_var * var
+    score = base_score + osc
+
+    p = softmax(score, tau=tau)
+
+    if prior is None:
+        return p
+
+    prior = np.asarray(prior, dtype=float)
+    prior = prior / max(prior.sum(), 1e-8)
+    p_mix = 0.8 * p + 0.2 * prior
+    p_mix = np.clip(p_mix, 1e-8, 1.0)
+    p_mix /= p_mix.sum()
+    return p_mix
diff --git a/Down/AntiDown/utils/logging.py b/Down/AntiDown/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..48eb53ef41067f626bbfcadaeabd2aaa10927896
--- /dev/null
+++ b/Down/AntiDown/utils/logging.py
@@ -0,0 +1,63 @@
+"""
+Simple logging utilities for experiments.
+
+Provides a minimal CSVLogger that creates the output directory if needed,
+writes a header once, and appends rows as dictionaries.
+"""
+
+from __future__ import annotations
+
+import csv
+import os
+from typing import Mapping, Sequence
+
+
+class CSVLogger:
+    """
+    Minimal CSV logger for experiment metrics.
+
+    Parameters
+    ----------
+    filepath : str
+        Path to the CSV file to create/append.
+    fieldnames : Sequence[str]
+        Ordered list of column names to use as the CSV header.
+
+    Notes
+    -----
+    - The directory containing `filepath` is created if it does not exist.
+    - The file is overwritten when the logger is constructed.
+    - Each call to `log` appends a single row.
+    """
+
+    def __init__(self, filepath: str, fieldnames: Sequence[str]) -> None:
+        self.filepath = filepath
+        # Handle the case where filepath is in the current directory.
+        directory = os.path.dirname(filepath)
+        if directory:
+            os.makedirs(directory, exist_ok=True)
+
+        self.fieldnames = list(fieldnames)
+        self._init_file()
+
+    def _init_file(self) -> None:
+        """Create or overwrite the CSV file and write the header row."""
+        with open(self.filepath, mode="w", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=self.fieldnames)
+            writer.writeheader()
+
+    def log(self, row_dict: Mapping[str, object]) -> None:
+        """
+        Append a single row to the CSV file.
+
+        Parameters
+        ----------
+        row_dict : Mapping[str, object]
+            Dictionary mapping field name to value. Missing keys will be
+            written as empty cells; extra keys are ignored.
+        """
+        # Project to known fieldnames to avoid surprises.
+        row = {k: row_dict.get(k, "") for k in self.fieldnames}
+        with open(self.filepath, mode="a", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=self.fieldnames)
+            writer.writerow(row)
diff --git a/Down/MODEL.md b/Down/MODEL.md
new file mode 100644
index 0000000000000000000000000000000000000000..572cb5e81dcdca5a592639a5614f09f644d11b23
--- /dev/null
+++ b/Down/MODEL.md
@@ -0,0 +1,96 @@
+\# Model Description: Down / AntiDown
+
+
+
+\## Purpose
+
+
+
+The Down / AntiDown models are reinforcement learning agents designed to expose and analyze failure modes under deceptive reward conditions. The emphasis is not on maximizing return, but on characterizing instability, collapse, and recovery dynamics.
+
+
+
+\## Down Agent
+
+
+
+\*\*Role:\*\*  
+
+The Down agent represents a learner operating under a corrupted reward signal that remains internally consistent but externally misaligned.
+
+
+
+\*\*Key Properties:\*\*
+
+\- Learns normally during early phases
+
+\- Maintains high confidence even as reward becomes deceptive
+
+\- Exhibits delayed collapse once misalignment compounds
+
+
+
+\*\*Observed Behaviors:\*\*
+
+\- Prolonged false stability
+
+\- Sharp performance cliffs
+
+\- Difficulty detecting reward corruption autonomously
+
+
+
+
+
+\## AntiDown Agent
+
+
+
+\*\*Role:\*\*  
+
+AntiDown serves as a contrasting hypothesis agent, exposed to the same environment but with altered sensitivity to reward inconsistencies.
+
+
+
+\*\*Key Properties:\*\*
+
+\- Increased sensitivity to instability signals
+
+\- Earlier behavioral deviation under reward corruption
+
+\- Serves as a comparative probe, not a “better” agent
+
+
+
+
+
+\## Design
+
+
+
+These models are not meant to be optimal. They are diagnostic instruments designed to surface questions such as:
+
+\- When does optimization become actively misleading?
+
+\- What internal signals precede collapse?
+
+\- Can instability be detected before outcomes degrade?
+
+
+
+\## Safety 
+
+
+
+Down / AntiDown formalize a common safety failure mode:
+
+> Systems that behave correctly until they suddenly don’t — and give no warning when it matters.
+
+
+
+They are intended as testbeds for studying monitoring, intervention, and robustness rather than performance.
+
+
+
+
+
diff --git a/Down/README.md b/Down/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7085883896b351c2895decb02c2756d8e431ebb6
--- /dev/null
+++ b/Down/README.md
@@ -0,0 +1,64 @@
+\# Reinforcement Learning under Deceptive Reward  
+\## Down / AntiDown
+
+This project investigates failure modes in reinforcement learning systems operating under deceptive, corrupted, or mis-specified reward signals. Rather than optimizing for peak performance, the focus is on observing how agents behave when reward feedback becomes unreliable while confidence and apparent stability remain high.
+
+The Down / AntiDown pair is designed as a controlled benchmark for studying robustness, collapse, and recovery dynamics under adversarial reward conditions, with direct relevance to AI safety and alignment research.
+
+\## Core Idea
+
+In many real-world and safety-critical settings, reward signals may be:
+
+\- Misaligned with the true task objective
+
+\- Delayed, noisy, or partially adversarial
+
+\- Temporarily valid and then corrupted
+
+
+This project studies how learning agents respond to these conditions, particularly:
+
+\- How long they continue to act confidently under corrupted reward
+
+\- Whether collapse occurs abruptly or gradually
+
+\- Whether recovery is possible once reward integrity is restored
+
+
+\## Project Structure
+
+\- `agents/`  
+
+#Implementations of the Down and AntiDown agents.
+
+\- `envs/`  
+
+#Custom environments modeling deceptive reward dynamics.
+
+\- `run\_all.py`  
+
+#Entry point to run both Down and AntiDown agents in a unified experimental setup.
+
+
+\## Running the Experiments
+
+This project is designed to run without notebooks. (I resorted to Anaconda)
+
+```bash
+
+conda activate your\_env
+
+python run\_all.py
+
+Relevance to AI Safety
+
+This benchmark targets a known blind spot in current ML evaluation: agents that appear competent while optimizing the wrong objective. It provides concrete tools to study:
+
+Misalignment under deceptive feedback
+
+Early-warning signals preceding collapse
+
+Limits of reward-centric evaluation
+
+The project directly supports research on stability-aware and intervention-based safety mechanisms.
+
diff --git a/Down/down/envs/env_paradox.py b/Down/down/envs/env_paradox.py
new file mode 100644
index 0000000000000000000000000000000000000000..390c902a252a022c555a707b6acb02a23fdeea95
--- /dev/null
+++ b/Down/down/envs/env_paradox.py
@@ -0,0 +1,193 @@
+
+import numpy as np
+from dataclasses import dataclass
+from typing import Tuple, Dict, Optional
+
+
+@dataclass(frozen=True)
+class ParadoxState:
+    """Structured view of the epistemic state."""
+    ambiguity_type: int  # 0..2
+    evidence_bin: int    # 0..2
+    risk_level: int      # 0..1
+    phase: int           # 1..3
+
+
+class EpistemicValleyEnv:
+    """
+    Epistemic Valley Inframe v2
+
+    Discrete one-step environment modelling epistemic risk under ambiguity.
+
+    State s = (ambiguity_type, evidence_bin, risk_level, phase)
+      - ambiguity_type: 0=lexical, 1=scope, 2=missing-data
+      - evidence_bin:   0=low, 1=medium, 2=high
+      - risk_level:     0=low, 1=high
+      - phase:          1, 2, or 3 (reward regime)
+
+    Actions:
+      0 = HOLD      (stay with current uncertainty)
+      1 = EXPAND    (seek more evidence / broader context)
+      2 = CLARIFY   (ask a targeted clarification)
+      3 = DEFER     (explicitly postpone the decision)
+      4 = ANSWER    (commit to an answer)
+
+    Episodes are single-step: reset() -> step(action) -> done=True.
+    """
+    # actions (kept as attributes so agents can introspect)
+    HOLD = 0
+    EXPAND = 1
+    CLARIFY = 2
+    DEFER = 3
+    ANSWER = 4
+
+    def __init__(self, phase: int = 1, seed: Optional[int] = None) -> None:
+        assert phase in (1, 2, 3), "phase must be 1, 2 or 3"
+        self.ambiguity_types = ["lexical", "scope", "missing-data"]
+        self.evidence_bins = ["low", "medium", "high"]
+        self.risk_levels = ["low", "high"]
+        self.phases = [1, 2, 3]
+
+        self.n_ambiguity = len(self.ambiguity_types)
+        self.n_evidence = len(self.evidence_bins)
+        self.n_risk = len(self.risk_levels)
+        self.n_phase = len(self.phases)
+
+        self.n_states = self.n_ambiguity * self.n_evidence * self.n_risk * self.n_phase
+        self.n_actions = 5
+
+        self.phase = phase
+        self.rng = np.random.default_rng(seed)
+        self.state: Optional[ParadoxState] = None
+        self.done: bool = False
+
+    # ------------------------------------------------------------------
+    # state indexing helpers
+    # ------------------------------------------------------------------
+    def encode_state(self, s: ParadoxState) -> int:
+        """Flatten ParadoxState into [0, n_states)."""
+        idx = s.ambiguity_type
+        idx = idx * self.n_evidence + s.evidence_bin
+        idx = idx * self.n_risk + s.risk_level
+        # phase is 1..3, internally we use 0..2
+        phase_index = s.phase - 1
+        idx = idx * self.n_phase + phase_index
+        return idx
+
+    def decode_state(self, idx: int) -> ParadoxState:
+        """Inverse of encode_state."""
+        phase_index = idx % self.n_phase
+        idx //= self.n_phase
+        risk_level = idx % self.n_risk
+        idx //= self.n_risk
+        evidence_bin = idx % self.n_evidence
+        idx //= self.n_evidence
+        ambiguity_type = idx
+        return ParadoxState(
+            ambiguity_type=int(ambiguity_type),
+            evidence_bin=int(evidence_bin),
+            risk_level=int(risk_level),
+            phase=int(phase_index + 1),
+        )
+
+    # ------------------------------------------------------------------
+    # core env API
+    # ------------------------------------------------------------------
+    def _sample_state(self) -> ParadoxState:
+        a = int(self.rng.integers(0, self.n_ambiguity))
+        e = int(self.rng.integers(0, self.n_evidence))
+        r = int(self.rng.integers(0, self.n_risk))
+        return ParadoxState(
+            ambiguity_type=a,
+            evidence_bin=e,
+            risk_level=r,
+            phase=self.phase,
+        )
+
+    def reset(self) -> Tuple[int, Dict]:
+        """Sample a new epistemic state and return its index + info."""
+        self.state = self._sample_state()
+        self.done = False
+        s_idx = self.encode_state(self.state)
+        return s_idx, {"state": self.state}
+
+    def step(self, action: int) -> Tuple[int, float, bool, Dict]:
+        """Apply one action, return (next_state_index, reward, done, info)."""
+        assert self.state is not None, "Call reset() before step()."
+        s = self.state
+        reward, failure_mode = self._reward(s, action)
+        self.done = True
+        info = {
+            "state": s,
+            "failure_mode": failure_mode,
+        }
+        # one-step episode; next state is irrelevant for this env
+        return self.encode_state(s), float(reward), self.done, info
+
+    # ------------------------------------------------------------------
+    # reward function
+    # ------------------------------------------------------------------
+    def _reward(self, s: ParadoxState, action: int) -> Tuple[float, str]:
+        """
+        Reward shaped by:
+          - evidence / risk alignment
+          - failure mode (overconfident vs overcautious)
+          - phase (reward regime and noise)
+
+        We implement a clean, reproducible version matching the narrative in
+        the original MODELS document: aggressive actions are good when
+        evidence is strong and risk is low, dangerous when evidence is weak
+        and risk is high, and conservative actions behave inversely.
+        """
+        e_str = self.evidence_bins[s.evidence_bin]
+        r_str = self.risk_levels[s.risk_level]
+        phase = s.phase
+
+        high_risk = (r_str == "high")
+        low_risk = (r_str == "low")
+        low_evidence = (e_str == "low")
+        high_evidence = (e_str == "high")
+
+        failure_mode = "neutral"
+        base = 0.0
+
+        # --- classify aggressive vs conservative actions ---
+        aggressive = action in (self.EXPAND, self.ANSWER)
+        conservative = action in (self.HOLD, self.DEFER)
+
+        # --- base reward logic before phase-specific shaping ---
+        if aggressive and high_risk and low_evidence:
+            # archetypal overconfidence: answer aggressively with little support
+            base = -2.0
+            failure_mode = "overconfident"
+        elif conservative and low_risk and high_evidence:
+            # archetypal overcautious: you could answer safely but you freeze
+            base = -0.8
+            failure_mode = "overcautious"
+        else:
+            # reasonably aligned choices
+            if aggressive and high_evidence and low_risk:
+                base = +2.0
+            elif aggressive and not (high_risk and low_evidence):
+                base = +0.8
+            elif conservative and high_risk and low_evidence:
+                base = +1.2
+            else:
+                base = 0.0
+
+        # --- phase-specific shaping ---
+        if phase == 1:
+            # Slight bonus for successful aggressive behaviour
+            if aggressive and base > 0:
+                base += 0.5
+        elif phase == 2:
+            # Harsher overconfidence penalty
+            if failure_mode == "overconfident":
+                base -= 1.0
+        elif phase == 3:
+            # Noisy feedback regime
+            noise = float(self.rng.normal(0.0, 0.5))
+            base += noise
+            base = float(np.clip(base, -3.0, 3.0))
+
+        return base, failure_mode
diff --git a/Down/down/exp/plot_paradox_results.py b/Down/down/exp/plot_paradox_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..008b5f98f0e882147f38f240a43560b1a4d42ca0
--- /dev/null
+++ b/Down/down/exp/plot_paradox_results.py
@@ -0,0 +1,82 @@
+"""Plot results produced by exp/run_paradox_tabular.py.
+
+Creates PNG plots under down/graphics/.
+"""
+
+from __future__ import annotations
+
+import csv
+import os
+from pathlib import Path
+from typing import Dict, List
+
+import matplotlib.pyplot as plt
+
+
+def _load_csv(path: Path) -> List[Dict[str, str]]:
+    with path.open("r", newline="") as f:
+        return list(csv.DictReader(f))
+
+
+def main() -> None:
+    quark_dir = Path(__file__).resolve().parents[1]  # .../down
+    results_dir = Path(os.environ.get("TWOQUARKS_RESULTS_DIR", (quark_dir / "results").as_posix()))
+    graphics_dir = Path(os.environ.get("TWOQUARKS_GRAPHICS_DIR", (quark_dir / "graphics").as_posix()))
+    results_dir.mkdir(parents=True, exist_ok=True)
+    graphics_dir.mkdir(parents=True, exist_ok=True)
+    results_csv = results_dir / "down_paradox_tabular_results.csv"
+
+    if not results_csv.exists():
+        raise FileNotFoundError(f"Missing results CSV: {results_csv}")
+
+    rows = _load_csv(results_csv)
+    if not rows:
+        raise RuntimeError(f"CSV is empty: {results_csv}")
+
+    # Normalize columns (older versions used episode_reward/rho_state)
+    def to_float(x: str) -> float:
+        try:
+            return float(x)
+        except Exception:
+            return float("nan")
+
+    # Build per-agent series
+    agents = sorted({r.get("agent", "unknown") for r in rows})
+    xs_all = [int(r.get("global_episode", r.get("episode", 0)) or 0) for r in rows]
+
+    # Episode reward plot
+    plt.figure()
+    for a in agents:
+        xs = [int(r.get("global_episode", r.get("episode", 0)) or 0) for r in rows if r.get("agent") == a]
+        ys = [to_float(r.get("episode_reward", r.get("return", "nan"))) for r in rows if r.get("agent") == a]
+        if xs:
+            plt.plot(xs, ys, label=a)
+    plt.xlabel("Global episode")
+    plt.ylabel("Episode reward")
+    plt.title("DOWN: reward over training")
+    plt.legend()
+    out1 = graphics_dir / "down_paradox_tabular_reward.png"
+    plt.savefig(out1, dpi=160, bbox_inches="tight")
+    plt.close()
+
+    # Rho plot (if present)
+    if any(("rho_state" in r) for r in rows) or any(("rho" in r) for r in rows):
+        plt.figure()
+        for a in agents:
+            xs = [int(r.get("global_episode", r.get("episode", 0)) or 0) for r in rows if r.get("agent") == a]
+            ys = [to_float(r.get("rho_state", r.get("rho", "nan"))) for r in rows if r.get("agent") == a]
+            if xs:
+                plt.plot(xs, ys, label=a)
+        plt.xlabel("Global episode")
+        plt.ylabel("rho")
+        plt.title("DOWN: rho over training")
+        plt.legend()
+        out2 = graphics_dir / "down_paradox_tabular_rho.png"
+        plt.savefig(out2, dpi=160, bbox_inches="tight")
+        plt.close()
+
+    print(f"Saved plots to {graphics_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Down/down/exp/run_paradox_ppo_hybrid.py b/Down/down/exp/run_paradox_ppo_hybrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1cb1abe59ca2c7d0aea80615b65a6a3c1d83823
--- /dev/null
+++ b/Down/down/exp/run_paradox_ppo_hybrid.py
@@ -0,0 +1,112 @@
+
+import csv
+from pathlib import Path
+
+import numpy as np
+
+from pathlib import Path
+import sys
+
+_THIS_DIR = Path(__file__).resolve().parent
+_QUARK_DIR = _THIS_DIR.parent
+if str(_QUARK_DIR) not in sys.path:
+    sys.path.insert(0, str(_QUARK_DIR))
+
+from envs.env_paradox import EpistemicValleyEnv
+from levo.agent_levo_paradox import HFLevoAgent, LevoParadoxIsomerAgent
+
+
+def run_ppo_hybrid(
+    out_csv: str | Path,
+    episodes_per_phase: int = 400,
+    seed: int = 2025,
+    update_every: int = 256,
+) -> None:
+    """
+    Train the Levo Paradox PPO Hybrid engine on Epistemic Valley and log results.
+
+    We keep the same CSV schema as the tabular runner, plus the PPO-specific
+    rho estimate for the visited state.
+    """
+    out_path = Path(out_csv)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    rng = np.random.default_rng(seed)
+
+    # probe env for shapes
+    probe_env = EpistemicValleyEnv(phase=1, seed=seed)
+    n_states = probe_env.n_states
+    n_actions = probe_env.n_actions
+
+    agent = LevoParadoxPPOHybrid(
+        n_states=n_states,
+        n_actions=n_actions,
+        seed=seed,
+    )
+
+    global_ep = 0
+    steps_since_update = 0
+
+    with out_path.open("w", newline="") as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=[
+                "global_episode",
+                "phase",
+                "episode",
+                "env_seed",
+                "agent",
+                "episode_reward",
+                "failure_mode",
+                "rho_state",
+            ],
+        )
+        writer.writeheader()
+
+        for phase in (1, 2, 3):
+            for local_ep in range(episodes_per_phase):
+                env_seed = int(rng.integers(0, 2**31 - 1))
+                env = EpistemicValleyEnv(phase=phase, seed=env_seed)
+
+                s_idx, info = env.reset()
+                action, logp, value_est, rho_est = agent.select_action(s_idx)
+                s_next_idx, reward, done, step_info = env.step(action)
+                failure_mode = step_info.get("failure_mode", "neutral")
+
+                agent.store_transition(
+                    s_idx=s_idx,
+                    action=action,
+                    reward=reward,
+                    log_prob=logp,
+                    value=value_est,
+                    rho=rho_est,
+                    failure_mode=failure_mode,
+                )
+
+                writer.writerow(
+                    {
+                        "global_episode": global_ep,
+                        "phase": phase,
+                        "episode": local_ep,
+                        "env_seed": env_seed,
+                        "agent": "LevoParadoxPPOHybrid",
+                        "episode_reward": float(reward),
+                        "failure_mode": failure_mode,
+                        "rho_state": rho_est,
+                    }
+                )
+
+                global_ep += 1
+                steps_since_update += 1
+
+                if steps_since_update >= update_every:
+                    agent.update()
+                    steps_since_update = 0
+
+        # final update with any remaining samples
+        if steps_since_update > 0:
+            agent.update()
+
+
+if __name__ == "__main__":
+    run_ppo_hybrid((_QUARK_DIR / "results/paradox_ppo_results.csv").as_posix(), episodes_per_phase=400)
diff --git a/Down/down/exp/run_paradox_tabular.py b/Down/down/exp/run_paradox_tabular.py
new file mode 100644
index 0000000000000000000000000000000000000000..61111335273e753ec5a1d80bc5e7ff7d7608bea7
--- /dev/null
+++ b/Down/down/exp/run_paradox_tabular.py
@@ -0,0 +1,124 @@
+
+# --- twoquarks bootstrap (path-stable imports) ---
+import sys
+from pathlib import Path
+_ROOT = Path(__file__).resolve().parents[1]  # project root (sibling of exp/)
+if str(_ROOT) not in sys.path:
+    sys.path.insert(0, str(_ROOT))
+# -------------------------------------------------
+
+import csv
+import os
+from pathlib import Path
+import sys
+
+import numpy as np
+
+# Robust imports regardless of where python is launched from.
+_THIS_DIR = Path(__file__).resolve().parent
+_QUARK_DIR = _THIS_DIR.parent  # .../down
+_PROJECT_DIR = _QUARK_DIR.parent  # .../Down
+
+if str(_QUARK_DIR) not in sys.path:
+    sys.path.insert(0, str(_QUARK_DIR))
+
+from envs.env_paradox import EpistemicValleyEnv
+from levo.agent_levo_paradox import HFLevoAgent, LevoParadoxIsomerAgent
+
+
+def run_experiment(
+    out_csv: str | Path,
+    episodes_per_phase: int = 400,
+    seed: int = 2025,
+) -> None:
+    """
+    Run HF-Levo and Levo Paradox tabular agents and log results to CSV.
+
+    The CSV schema is:
+
+        global_episode,phase,episode,env_seed,agent,episode_reward,failure_mode,rho_state
+    """
+    out_path = Path(out_csv)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    rng = np.random.default_rng(seed)
+
+    # env for shape information only
+    probe_env = EpistemicValleyEnv(phase=1, seed=seed)
+    n_states = probe_env.n_states
+    n_actions = probe_env.n_actions
+
+    agents = [
+        HFLevoAgent(n_states, n_actions, seed=seed),
+        LevoParadoxIsomerAgent(n_states, n_actions, seed=seed + 1),
+    ]
+    agent_names = ["HFLevo", "LevoParadoxIsomer"]
+
+    global_ep = 0
+
+    with out_path.open("w", newline="") as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=[
+                "global_episode",
+                "phase",
+                "episode",
+                "env_seed",
+                "agent",
+                "episode_reward",
+                "failure_mode",
+                "rho_state",
+            ],
+        )
+        writer.writeheader()
+
+        for phase in (1, 2, 3):
+            for local_ep in range(episodes_per_phase):
+                env_seed = int(rng.integers(0, 2**31 - 1))
+
+                for agent, name in zip(agents, agent_names, strict=True):
+                    env = EpistemicValleyEnv(phase=phase, seed=env_seed)
+                    s_idx, info = env.reset()
+                    a = agent.select_action(s_idx)
+                    s_next_idx, reward, done, step_info = env.step(a)
+
+                    failure_mode = step_info.get("failure_mode", "neutral")
+
+                    rho_state = (
+                        float(getattr(agent, "rho")[s_idx])  # type: ignore[attr-defined]
+                        if hasattr(agent, "rho")
+                        else float("nan")
+                    )
+
+                    agent.update(
+                        s_idx=s_idx,
+                        a=a,
+                        r=reward,
+                        s_next_idx=s_next_idx,
+                        done=done,
+                        failure_mode=failure_mode,
+                    )
+
+                    writer.writerow(
+                        {
+                            "global_episode": global_ep,
+                            "phase": phase,
+                            "episode": local_ep,
+                            "env_seed": env_seed,
+                            "agent": name,
+                            "episode_reward": float(reward),
+                            "failure_mode": failure_mode,
+                            "rho_state": rho_state,
+                        }
+                    )
+
+                global_ep += 1
+
+
+if __name__ == "__main__":
+    # Allow a caller (e.g., run_all.py) to route outputs to a shared folder.
+    _results_dir = Path(os.environ.get("TWOQUARKS_RESULTS_DIR", (_QUARK_DIR / "results").as_posix()))
+    _results_dir.mkdir(parents=True, exist_ok=True)
+    _out_csv = _results_dir / "down_paradox_tabular_results.csv"
+    run_experiment(_out_csv.as_posix(), episodes_per_phase=400)
diff --git a/Down/down/levo/agent_levo_paradox.py b/Down/down/levo/agent_levo_paradox.py
new file mode 100644
index 0000000000000000000000000000000000000000..246dd893d9d047e47736aa3b131c6200e4dcbae7
--- /dev/null
+++ b/Down/down/levo/agent_levo_paradox.py
@@ -0,0 +1,399 @@
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import List, Tuple, Optional, Dict
+
+import numpy as np
+
+try:
+    import torch
+    import torch.nn as nn
+    import torch.optim as optim
+except Exception:  # pragma: no cover - torch might not be installed
+    torch = None  # type: ignore[assignment]
+    nn = object  # type: ignore[assignment]
+    optim = object  # type: ignore[assignment]
+
+
+# ---------------------------------------------------------------------------
+# Tabular baselines (HF-Levo + isomeric Levo Paradox)
+# ---------------------------------------------------------------------------
+
+
+class HFLevoAgent:
+    """
+    Single-head HF-Levo baseline agent.
+
+    A simple Q-learning agent whose exploration temperature is modulated
+    by a high-frequency term:
+
+        T_t = tau * (1 + A * sin(omega * t))
+
+    This creates oscillatory exploration / exploitation cycles.
+    """
+
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        A: float = 0.5,
+        omega: float = 0.05,
+        tau: float = 0.7,
+        alpha: float = 0.1,
+        gamma: float = 0.95,
+        seed: Optional[int] = None,
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.A = A
+        self.omega = omega
+        self.tau = tau
+        self.alpha = alpha
+        self.gamma = gamma
+
+        self.Q = np.zeros((n_states, n_actions), dtype=np.float32)
+        self.rng = np.random.default_rng(seed)
+        self.t = 0
+
+    def _temperature(self) -> float:
+        return float(self.tau * (1.0 + self.A * math.sin(self.omega * self.t)))
+
+    def _softmax(self, q: np.ndarray, temp: float) -> np.ndarray:
+        z = q - np.max(q)
+        e = np.exp(z / max(temp, 1e-6))
+        return e / e.sum()
+
+    def select_action(self, s_idx: int) -> int:
+        temp = self._temperature()
+        probs = self._softmax(self.Q[s_idx], temp)
+        a = int(self.rng.choice(self.n_actions, p=probs))
+        self.t += 1
+        return a
+
+    def update(
+        self,
+        s_idx: int,
+        a: int,
+        r: float,
+        s_next_idx: int,
+        done: bool,
+        failure_mode: str,
+    ) -> None:
+        q = self.Q
+        target = r if done else (r + self.gamma * float(np.max(q[s_next_idx])))
+        delta = target - float(q[s_idx, a])
+        q[s_idx, a] += self.alpha * delta
+
+
+class LevoParadoxIsomerAgent:
+    """
+    Isomeric tabular agent with conservative and aggressive Q-functions.
+
+    Two heads Q_L (conservative) and Q_R (aggressive) are mixed by a
+    contextual polarization rho[s] in [0, 1]:
+
+        Q_mix[s] = (1 - rho[s]) * Q_L[s] + rho[s] * Q_R[s]
+
+    Polarization is nudged by failure modes reported by the environment:
+      - "overconfident"  -> push rho down (more conservative)
+      - "overcautious"   -> push rho up   (more aggressive)
+    """
+
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        tau: float = 0.7,
+        alpha: float = 0.1,
+        gamma: float = 0.95,
+        eta: float = 0.05,
+        seed: Optional[int] = None,
+    ) -> None:
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.tau = tau
+        self.alpha = alpha
+        self.gamma = gamma
+        self.eta = eta
+
+        self.Q_L = np.zeros((n_states, n_actions), dtype=np.float32)
+        self.Q_R = np.zeros((n_states, n_actions), dtype=np.float32)
+        self.rho = np.full(n_states, 0.5, dtype=np.float32)
+
+        self.rng = np.random.default_rng(seed)
+
+    def _softmax(self, x: np.ndarray) -> np.ndarray:
+        z = x - np.max(x)
+        e = np.exp(z / max(self.tau, 1e-6))
+        return e / e.sum()
+
+    def select_action(self, s_idx: int) -> int:
+        rho_s = float(self.rho[s_idx])
+        q_mix = (1.0 - rho_s) * self.Q_L[s_idx] + rho_s * self.Q_R[s_idx]
+        probs = self._softmax(q_mix)
+        a = int(self.rng.choice(self.n_actions, p=probs))
+        return a
+
+    def update(
+        self,
+        s_idx: int,
+        a: int,
+        r: float,
+        s_next_idx: int,
+        done: bool,
+        failure_mode: str,
+    ) -> None:
+        # choose learning head: conservative for overconfidence,
+        # aggressive for overcautious, otherwise both share credit.
+        if failure_mode == "overconfident":
+            heads = ("L",)
+        elif failure_mode == "overcautious":
+            heads = ("R",)
+        else:
+            heads = ("L", "R")
+
+        for h in heads:
+            Q = self.Q_L if h == "L" else self.Q_R
+            target = r if done else (r + self.gamma * float(np.max(Q[s_next_idx])))
+            delta = target - float(Q[s_idx, a])
+            Q[s_idx, a] += self.alpha * delta
+
+        # polarization update
+        if failure_mode == "overconfident":
+            self.rho[s_idx] = np.clip(self.rho[s_idx] - self.eta, 0.0, 1.0)
+        elif failure_mode == "overcautious":
+            self.rho[s_idx] = np.clip(self.rho[s_idx] + self.eta, 0.0, 1.0)
+
+
+# ---------------------------------------------------------------------------
+# PPO Hybrid Engine (GPU-ready, isomeric actor-critic)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class Transition:
+    state_idx: int
+    action: int
+    reward: float
+    log_prob: float
+    value: float
+    rho: float
+    failure_mode: str
+
+
+class ParadoxActorCritic(nn.Module):  # type: ignore[misc]
+    """
+    Isomeric actor-critic.
+
+    - shared MLP trunk over a one-hot encoding of the discrete state
+    - two actor heads: conservative vs aggressive
+    - one scalar gating head producing rho(s) in [0, 1]
+    - one critic head V(s)
+
+    The final policy is a mixture of the two isomers, combined inside the
+    logits space and fed through softmax.
+    """
+
+    def __init__(self, n_states: int, n_actions: int, hidden_dim: int = 128) -> None:
+        super().__init__()
+        self.n_states = n_states
+        self.n_actions = n_actions
+
+        self.trunk = nn.Sequential(
+            nn.Linear(n_states, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+        )
+        self.actor_cons = nn.Linear(hidden_dim, n_actions)
+        self.actor_aggr = nn.Linear(hidden_dim, n_actions)
+        self.gate = nn.Linear(hidden_dim, 1)
+        self.critic = nn.Linear(hidden_dim, 1)
+
+    def forward(self, state_one_hot: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        z = self.trunk(state_one_hot)
+        logits_cons = self.actor_cons(z)
+        logits_aggr = self.actor_aggr(z)
+        rho = torch.sigmoid(self.gate(z))  # [B, 1]
+        value = self.critic(z).squeeze(-1)  # [B]
+
+        # mixture in logit space
+        logits_mix = (1.0 - rho) * logits_cons + rho * logits_aggr
+        return logits_mix, value, rho.squeeze(-1), logits_cons * 1.0  # last term unused but can help debugging
+
+
+class LevoParadoxPPOHybrid:
+    """
+    GPU-ready PPO hybrid engine with isomeric policy.
+
+    This agent combines:
+      - an isomeric actor (conservative + aggressive heads),
+      - a gating network rho(s) trained end-to-end,
+      - PPO-style clipped policy updates,
+      - and a value baseline for variance reduction.
+
+    It operates directly on the discrete Epistemic Valley state space using
+    a one-hot encoding, which keeps things simple and fully reproducible.
+    """
+
+    def __init__(
+        self,
+        n_states: int,
+        n_actions: int,
+        gamma: float = 0.99,
+        lam: float = 0.95,
+        clip_eps: float = 0.2,
+        entropy_coef: float = 0.01,
+        value_coef: float = 0.5,
+        lr: float = 3e-4,
+        batch_size: int = 256,
+        update_epochs: int = 8,
+        seed: Optional[int] = None,
+    ) -> None:
+        if torch is None:
+            raise RuntimeError("PyTorch is required for LevoParadoxPPOHybrid.")
+
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.gamma = gamma
+        self.lam = lam
+        self.clip_eps = clip_eps
+        self.entropy_coef = entropy_coef
+        self.value_coef = value_coef
+        self.batch_size = batch_size
+        self.update_epochs = update_epochs
+
+        if seed is not None:
+            torch.manual_seed(seed)
+            np.random.seed(seed)
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.net = ParadoxActorCritic(n_states, n_actions).to(self.device)
+        self.optimizer = optim.Adam(self.net.parameters(), lr=lr)
+
+        self.buffer: List[Transition] = []
+
+    # ---------------------------- utilities -----------------------------
+
+    def _one_hot(self, idx: np.ndarray | int) -> torch.Tensor:
+        idx_arr = np.atleast_1d(idx).astype(np.int64)
+        x = np.zeros((idx_arr.shape[0], self.n_states), dtype=np.float32)
+        x[np.arange(idx_arr.shape[0]), idx_arr] = 1.0
+        return torch.from_numpy(x).to(self.device)
+
+    # ---------------------------- interaction ---------------------------
+
+    def select_action(self, s_idx: int) -> Tuple[int, float, float, float]:
+        """Return (action, log_prob, value_estimate, rho)."""
+        self.net.eval()
+        state_one_hot = self._one_hot(s_idx)
+        logits_mix, value, rho, _ = self.net(state_one_hot)  # type: ignore[misc]
+        dist = torch.distributions.Categorical(logits=logits_mix)
+        action = dist.sample()
+        log_prob = dist.log_prob(action)
+        return int(action.item()), float(log_prob.item()), float(value.item()), float(rho.item())
+
+    def store_transition(
+        self,
+        s_idx: int,
+        action: int,
+        reward: float,
+        log_prob: float,
+        value: float,
+        rho: float,
+        failure_mode: str,
+    ) -> None:
+        self.buffer.append(
+            Transition(
+                state_idx=int(s_idx),
+                action=int(action),
+                reward=float(reward),
+                log_prob=float(log_prob),
+                value=float(value),
+                rho=float(rho),
+                failure_mode=failure_mode,
+            )
+        )
+
+    # ---------------------------- learning ------------------------------
+
+    def _compute_advantages(
+        self, rewards: np.ndarray, values: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        # For one-step episodes, advantage = reward - value, return = reward
+        returns = rewards.copy()
+        advantages = rewards - values
+        # Normalise for stability
+        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+        return returns, advantages
+
+    def update(self) -> Dict[str, float]:
+        if not self.buffer:
+            return {}
+
+        # Collect batch
+        states = np.array([t.state_idx for t in self.buffer], dtype=np.int64)
+        actions = np.array([t.action for t in self.buffer], dtype=np.int64)
+        rewards = np.array([t.reward for t in self.buffer], dtype=np.float32)
+        old_log_probs = np.array([t.log_prob for t in self.buffer], dtype=np.float32)
+        values = np.array([t.value for t in self.buffer], dtype=np.float32)
+
+        returns, advantages = self._compute_advantages(rewards, values)
+
+        # Convert to tensors
+        states_t = self._one_hot(states)
+        actions_t = torch.from_numpy(actions).to(self.device)
+        returns_t = torch.from_numpy(returns).to(self.device)
+        advantages_t = torch.from_numpy(advantages).to(self.device)
+        old_log_probs_t = torch.from_numpy(old_log_probs).to(self.device)
+
+        dataset_size = states_t.size(0)
+        idxs = np.arange(dataset_size)
+
+        stats: Dict[str, float] = {}
+
+        self.net.train()
+        for _ in range(self.update_epochs):
+            np.random.shuffle(idxs)
+            for start in range(0, dataset_size, self.batch_size):
+                batch_idx = idxs[start : start + self.batch_size]
+                if len(batch_idx) == 0:
+                    continue
+
+                batch_states = states_t[batch_idx]
+                batch_actions = actions_t[batch_idx]
+                batch_returns = returns_t[batch_idx]
+                batch_adv = advantages_t[batch_idx]
+                batch_old_logp = old_log_probs_t[batch_idx]
+
+                logits_mix, values_pred, _, _ = self.net(batch_states)  # type: ignore[misc]
+                dist = torch.distributions.Categorical(logits=logits_mix)
+                log_probs = dist.log_prob(batch_actions)
+                entropy = dist.entropy().mean()
+
+                # PPO clipped objective
+                ratio = torch.exp(log_probs - batch_old_logp)
+                unclipped = ratio * batch_adv
+                clipped = torch.clamp(ratio, 1.0 - self.clip_eps, 1.0 + self.clip_eps) * batch_adv
+                policy_loss = -torch.min(unclipped, clipped).mean()
+
+                value_loss = (batch_returns - values_pred).pow(2).mean()
+
+                loss = policy_loss + self.value_coef * value_loss - self.entropy_coef * entropy
+
+                self.optimizer.zero_grad()
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(self.net.parameters(), max_norm=1.0)
+                self.optimizer.step()
+
+                stats = {
+                    "policy_loss": float(policy_loss.item()),
+                    "value_loss": float(value_loss.item()),
+                    "entropy": float(entropy.item()),
+                }
+
+        # clear buffer
+        self.buffer.clear()
+        return stats
diff --git a/Down/down/results/paradox_ppo_results.csv b/Down/down/results/paradox_ppo_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3bf76532044344a45f983d5ad953bf7687df3941
--- /dev/null
+++ b/Down/down/results/paradox_ppo_results.csv
@@ -0,0 +1,1201 @@
+global_episode,phase,episode,env_seed,agent,episode_reward,failure_mode,rho_state
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+177,1,177,600780774,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+178,1,178,389237547,LevoParadoxPPOHybrid,1.3,neutral,0.4752705693244934
+179,1,179,531352528,LevoParadoxPPOHybrid,0.0,neutral,0.4740993082523346
+180,1,180,308998320,LevoParadoxPPOHybrid,1.2,neutral,0.47555792331695557
+181,1,181,133112608,LevoParadoxPPOHybrid,0.0,neutral,0.47702598571777344
+182,1,182,1335500866,LevoParadoxPPOHybrid,0.0,neutral,0.47656360268592834
+183,1,183,1455247303,LevoParadoxPPOHybrid,2.5,neutral,0.4758386015892029
+184,1,184,818574077,LevoParadoxPPOHybrid,-2.0,overconfident,0.4746558368206024
+185,1,185,783962675,LevoParadoxPPOHybrid,0.0,neutral,0.4743559956550598
+186,1,186,181722168,LevoParadoxPPOHybrid,-0.8,overcautious,0.4768047034740448
+187,1,187,922465315,LevoParadoxPPOHybrid,1.3,neutral,0.47567713260650635
+188,1,188,1058682574,LevoParadoxPPOHybrid,0.0,neutral,0.4784092605113983
+189,1,189,2126397323,LevoParadoxPPOHybrid,0.0,neutral,0.4752805233001709
+190,1,190,23796221,LevoParadoxPPOHybrid,0.0,neutral,0.4743559956550598
+191,1,191,677074079,LevoParadoxPPOHybrid,0.0,neutral,0.4810570776462555
+192,1,192,1977263236,LevoParadoxPPOHybrid,-2.0,overconfident,0.47555792331695557
+193,1,193,1768875981,LevoParadoxPPOHybrid,0.0,neutral,0.47838863730430603
+194,1,194,1212545188,LevoParadoxPPOHybrid,2.5,neutral,0.4758386015892029
+195,1,195,1186674390,LevoParadoxPPOHybrid,1.3,neutral,0.47656360268592834
+196,1,196,1056632766,LevoParadoxPPOHybrid,0.0,neutral,0.4752805233001709
+197,1,197,268535661,LevoParadoxPPOHybrid,1.3,neutral,0.4752705693244934
+198,1,198,888167157,LevoParadoxPPOHybrid,1.2,neutral,0.4743559956550598
+199,1,199,530391691,LevoParadoxPPOHybrid,1.3,neutral,0.48266562819480896
+200,1,200,50594990,LevoParadoxPPOHybrid,2.5,neutral,0.4758386015892029
+201,1,201,222395105,LevoParadoxPPOHybrid,-2.0,overconfident,0.47555792331695557
+202,1,202,287284573,LevoParadoxPPOHybrid,0.0,neutral,0.4752705693244934
+203,1,203,514973304,LevoParadoxPPOHybrid,0.0,neutral,0.4752705693244934
+204,1,204,1909475802,LevoParadoxPPOHybrid,-0.8,overcautious,0.4810570776462555
+205,1,205,1879525266,LevoParadoxPPOHybrid,-2.0,overconfident,0.4743559956550598
+206,1,206,1420380137,LevoParadoxPPOHybrid,-0.8,overcautious,0.4810570776462555
+207,1,207,1737331410,LevoParadoxPPOHybrid,0.0,neutral,0.4789677560329437
+208,1,208,1064923684,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+209,1,209,1238273317,LevoParadoxPPOHybrid,0.0,neutral,0.4789677560329437
+210,1,210,144185798,LevoParadoxPPOHybrid,1.3,neutral,0.48266562819480896
+211,1,211,575162410,LevoParadoxPPOHybrid,0.0,neutral,0.4780098497867584
+212,1,212,707283582,LevoParadoxPPOHybrid,-0.8,overcautious,0.4758386015892029
+213,1,213,1405835406,LevoParadoxPPOHybrid,0.0,neutral,0.4740993082523346
+214,1,214,789235422,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+215,1,215,1388271326,LevoParadoxPPOHybrid,0.0,neutral,0.4752805233001709
+216,1,216,1865718892,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+217,1,217,1293915651,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+218,1,218,1428150647,LevoParadoxPPOHybrid,0.0,neutral,0.4810570776462555
+219,1,219,19248056,LevoParadoxPPOHybrid,-0.8,overcautious,0.4758386015892029
+220,1,220,491031830,LevoParadoxPPOHybrid,1.3,neutral,0.47567713260650635
+221,1,221,636126198,LevoParadoxPPOHybrid,1.3,neutral,0.4789677560329437
+222,1,222,456728828,LevoParadoxPPOHybrid,0.0,neutral,0.4743559956550598
+223,1,223,966697912,LevoParadoxPPOHybrid,0.0,neutral,0.47555792331695557
+224,1,224,1166498720,LevoParadoxPPOHybrid,0.0,neutral,0.47656360268592834
+225,1,225,1571014065,LevoParadoxPPOHybrid,2.5,neutral,0.4768047034740448
+226,1,226,714126436,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+227,1,227,1206418342,LevoParadoxPPOHybrid,0.0,neutral,0.47656360268592834
+228,1,228,1372090007,LevoParadoxPPOHybrid,-2.0,overconfident,0.4743559956550598
+229,1,229,769009050,LevoParadoxPPOHybrid,2.5,neutral,0.4758386015892029
+230,1,230,1353001314,LevoParadoxPPOHybrid,0.0,neutral,0.47702598571777344
+231,1,231,1733327736,LevoParadoxPPOHybrid,1.3,neutral,0.4752705693244934
+232,1,232,638285556,LevoParadoxPPOHybrid,1.3,neutral,0.48266562819480896
+233,1,233,988454733,LevoParadoxPPOHybrid,0.0,neutral,0.4784092605113983
+234,1,234,1459581848,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+235,1,235,1106839428,LevoParadoxPPOHybrid,1.2,neutral,0.47555792331695557
+236,1,236,388494700,LevoParadoxPPOHybrid,-2.0,overconfident,0.47555792331695557
+237,1,237,1441804184,LevoParadoxPPOHybrid,0.0,neutral,0.4789677560329437
+238,1,238,683008548,LevoParadoxPPOHybrid,1.3,neutral,0.4784092605113983
+239,1,239,557447257,LevoParadoxPPOHybrid,0.0,neutral,0.4789677560329437
+240,1,240,1553434256,LevoParadoxPPOHybrid,-2.0,overconfident,0.4746558368206024
+241,1,241,1196216329,LevoParadoxPPOHybrid,0.0,neutral,0.4740993082523346
+242,1,242,205445195,LevoParadoxPPOHybrid,0.0,neutral,0.47567713260650635
+243,1,243,138987130,LevoParadoxPPOHybrid,1.3,neutral,0.48266562819480896
+244,1,244,1601279830,LevoParadoxPPOHybrid,0.0,neutral,0.4752805233001709
+245,1,245,523497866,LevoParadoxPPOHybrid,0.0,neutral,0.4768047034740448
+246,1,246,1681903654,LevoParadoxPPOHybrid,1.3,neutral,0.4772508144378662
+247,1,247,1447758672,LevoParadoxPPOHybrid,0.0,neutral,0.4784092605113983
+248,1,248,1263090791,LevoParadoxPPOHybrid,0.0,neutral,0.4746558368206024
+249,1,249,2069934169,LevoParadoxPPOHybrid,1.3,neutral,0.4752705693244934
+250,1,250,1175589659,LevoParadoxPPOHybrid,0.0,neutral,0.4746558368206024
+251,1,251,792436927,LevoParadoxPPOHybrid,1.3,neutral,0.4789677560329437
+252,1,252,77793463,LevoParadoxPPOHybrid,1.3,neutral,0.47567713260650635
+253,1,253,1178447001,LevoParadoxPPOHybrid,0.0,neutral,0.4752805233001709
+254,1,254,1867902247,LevoParadoxPPOHybrid,1.3,neutral,0.47702598571777344
+255,1,255,1132483487,LevoParadoxPPOHybrid,0.0,neutral,0.48266562819480896
+256,1,256,1108550737,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+257,1,257,761845810,LevoParadoxPPOHybrid,0.0,neutral,0.47729626297950745
+258,1,258,2026103214,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+259,1,259,1320383576,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+260,1,260,165278918,LevoParadoxPPOHybrid,1.2,neutral,0.4783268868923187
+261,1,261,1264440670,LevoParadoxPPOHybrid,-0.8,overcautious,0.4791228175163269
+262,1,262,859892894,LevoParadoxPPOHybrid,1.3,neutral,0.48010820150375366
+263,1,263,411458685,LevoParadoxPPOHybrid,0.0,neutral,0.479666531085968
+264,1,264,1931263994,LevoParadoxPPOHybrid,0.0,neutral,0.4829937219619751
+265,1,265,213511530,LevoParadoxPPOHybrid,2.5,neutral,0.48367613554000854
+266,1,266,1137608613,LevoParadoxPPOHybrid,-2.0,overconfident,0.4773579239845276
+267,1,267,843567272,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+268,1,268,11175988,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+269,1,269,687107894,LevoParadoxPPOHybrid,1.3,neutral,0.47729626297950745
+270,1,270,291918902,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+271,1,271,1087591275,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+272,1,272,1813734633,LevoParadoxPPOHybrid,0.0,neutral,0.4791228175163269
+273,1,273,461816730,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+274,1,274,441050625,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+275,1,275,2141182885,LevoParadoxPPOHybrid,0.0,neutral,0.4808143377304077
+276,1,276,749647573,LevoParadoxPPOHybrid,-0.8,overcautious,0.4791228175163269
+277,1,277,702601389,LevoParadoxPPOHybrid,1.3,neutral,0.4818275570869446
+278,1,278,500813039,LevoParadoxPPOHybrid,1.3,neutral,0.4829937219619751
+279,1,279,1327850191,LevoParadoxPPOHybrid,0.0,neutral,0.4788035750389099
+280,1,280,1479353871,LevoParadoxPPOHybrid,0.0,neutral,0.48367613554000854
+281,1,281,875883029,LevoParadoxPPOHybrid,-0.8,overcautious,0.4791228175163269
+282,1,282,956514865,LevoParadoxPPOHybrid,0.0,neutral,0.4773579239845276
+283,1,283,140168808,LevoParadoxPPOHybrid,2.5,neutral,0.48367613554000854
+284,1,284,2023927855,LevoParadoxPPOHybrid,0.0,neutral,0.4777975380420685
+285,1,285,1850097089,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+286,1,286,366267895,LevoParadoxPPOHybrid,1.3,neutral,0.48010820150375366
+287,1,287,833125277,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+288,1,288,1062058470,LevoParadoxPPOHybrid,-0.8,overcautious,0.48367613554000854
+289,1,289,755011055,LevoParadoxPPOHybrid,1.3,neutral,0.4805646538734436
+290,1,290,1193341725,LevoParadoxPPOHybrid,0.0,neutral,0.4784007668495178
+291,1,291,47566427,LevoParadoxPPOHybrid,1.3,neutral,0.4829937219619751
+292,1,292,1480907230,LevoParadoxPPOHybrid,1.3,neutral,0.4818275570869446
+293,1,293,1193747826,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+294,1,294,188056822,LevoParadoxPPOHybrid,1.3,neutral,0.479666531085968
+295,1,295,2058188731,LevoParadoxPPOHybrid,1.3,neutral,0.4784007668495178
+296,1,296,131903334,LevoParadoxPPOHybrid,2.5,neutral,0.48367613554000854
+297,1,297,2023999375,LevoParadoxPPOHybrid,0.0,neutral,0.4782043993473053
+298,1,298,14284283,LevoParadoxPPOHybrid,1.3,neutral,0.4829937219619751
+299,1,299,430728515,LevoParadoxPPOHybrid,1.3,neutral,0.4818275570869446
+300,1,300,1773637227,LevoParadoxPPOHybrid,0.0,neutral,0.4784007668495178
+301,1,301,1447643177,LevoParadoxPPOHybrid,2.5,neutral,0.4791228175163269
+302,1,302,980890604,LevoParadoxPPOHybrid,-0.8,overcautious,0.4791228175163269
+303,1,303,1738676116,LevoParadoxPPOHybrid,2.5,neutral,0.48367613554000854
+304,1,304,202567723,LevoParadoxPPOHybrid,1.3,neutral,0.479666531085968
+305,1,305,1153805088,LevoParadoxPPOHybrid,1.2,neutral,0.4783268868923187
+306,1,306,972971566,LevoParadoxPPOHybrid,-2.0,overconfident,0.4783268868923187
+307,1,307,1472596543,LevoParadoxPPOHybrid,-0.8,overcautious,0.48367613554000854
+308,1,308,1472317967,LevoParadoxPPOHybrid,1.3,neutral,0.48244643211364746
+309,1,309,215008918,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+310,1,310,1105771947,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+311,1,311,652569339,LevoParadoxPPOHybrid,0.0,neutral,0.4805646538734436
+312,1,312,1726057777,LevoParadoxPPOHybrid,-0.8,overcautious,0.48367613554000854
+313,1,313,1485944499,LevoParadoxPPOHybrid,1.3,neutral,0.48244643211364746
+314,1,314,294205674,LevoParadoxPPOHybrid,0.0,neutral,0.479666531085968
+315,1,315,1016871026,LevoParadoxPPOHybrid,0.0,neutral,0.4783268868923187
+316,1,316,1168910589,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+317,1,317,1517292813,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+318,1,318,2030522066,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+319,1,319,959589186,LevoParadoxPPOHybrid,1.2,neutral,0.4773579239845276
+320,1,320,590193953,LevoParadoxPPOHybrid,1.3,neutral,0.479666531085968
+321,1,321,675005705,LevoParadoxPPOHybrid,0.0,neutral,0.4777975380420685
+322,1,322,925232956,LevoParadoxPPOHybrid,0.0,neutral,0.4784007668495178
+323,1,323,1408472699,LevoParadoxPPOHybrid,-0.8,overcautious,0.4791228175163269
+324,1,324,859189252,LevoParadoxPPOHybrid,1.3,neutral,0.4777975380420685
+325,1,325,399323533,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+326,1,326,980315658,LevoParadoxPPOHybrid,-2.0,overconfident,0.4788035750389099
+327,1,327,123987181,LevoParadoxPPOHybrid,1.2,neutral,0.4773579239845276
+328,1,328,409996997,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+329,1,329,708892450,LevoParadoxPPOHybrid,-2.0,overconfident,0.4783268868923187
+330,1,330,1373932302,LevoParadoxPPOHybrid,1.3,neutral,0.47729626297950745
+331,1,331,2077742390,LevoParadoxPPOHybrid,1.3,neutral,0.4784007668495178
+332,1,332,761108960,LevoParadoxPPOHybrid,0.0,neutral,0.4784007668495178
+333,1,333,1211512608,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+334,1,334,1884662523,LevoParadoxPPOHybrid,0.0,neutral,0.4777975380420685
+335,1,335,2002191522,LevoParadoxPPOHybrid,0.0,neutral,0.4863224923610687
+336,1,336,2028646946,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+337,1,337,589112024,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+338,1,338,610703030,LevoParadoxPPOHybrid,0.0,neutral,0.4802131950855255
+339,1,339,972829234,LevoParadoxPPOHybrid,0.0,neutral,0.4777975380420685
+340,1,340,465154269,LevoParadoxPPOHybrid,2.5,neutral,0.4782043993473053
+341,1,341,470901255,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+342,1,342,1990213325,LevoParadoxPPOHybrid,0.0,neutral,0.47729626297950745
+343,1,343,1646775850,LevoParadoxPPOHybrid,0.0,neutral,0.4829937219619751
+344,1,344,1057311607,LevoParadoxPPOHybrid,1.3,neutral,0.4802131950855255
+345,1,345,590319545,LevoParadoxPPOHybrid,0.0,neutral,0.4802131950855255
+346,1,346,516318123,LevoParadoxPPOHybrid,1.3,neutral,0.47729626297950745
+347,1,347,70890343,LevoParadoxPPOHybrid,0.0,neutral,0.47729626297950745
+348,1,348,804413128,LevoParadoxPPOHybrid,2.5,neutral,0.4782043993473053
+349,1,349,1504695760,LevoParadoxPPOHybrid,1.3,neutral,0.4805646538734436
+350,1,350,940669613,LevoParadoxPPOHybrid,0.0,neutral,0.4808143377304077
+351,1,351,821018278,LevoParadoxPPOHybrid,1.3,neutral,0.48010820150375366
+352,1,352,2080007758,LevoParadoxPPOHybrid,0.0,neutral,0.48367613554000854
+353,1,353,461373511,LevoParadoxPPOHybrid,0.0,neutral,0.4808143377304077
+354,1,354,1987269884,LevoParadoxPPOHybrid,0.0,neutral,0.4805646538734436
+355,1,355,885162807,LevoParadoxPPOHybrid,1.3,neutral,0.4863224923610687
+356,1,356,779768070,LevoParadoxPPOHybrid,0.0,neutral,0.4802131950855255
+357,1,357,966063187,LevoParadoxPPOHybrid,-2.0,overconfident,0.4783268868923187
+358,1,358,1359588046,LevoParadoxPPOHybrid,1.3,neutral,0.4808143377304077
+359,1,359,393633121,LevoParadoxPPOHybrid,0.0,neutral,0.4802131950855255
+360,1,360,991070115,LevoParadoxPPOHybrid,1.3,neutral,0.4818275570869446
+361,1,361,1695833246,LevoParadoxPPOHybrid,1.3,neutral,0.4808143377304077
+362,1,362,259218709,LevoParadoxPPOHybrid,0.0,neutral,0.4773579239845276
+363,1,363,1252523270,LevoParadoxPPOHybrid,1.2,neutral,0.4788035750389099
+364,1,364,618943155,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+365,1,365,1149281050,LevoParadoxPPOHybrid,1.3,neutral,0.4805646538734436
+366,1,366,605383710,LevoParadoxPPOHybrid,1.3,neutral,0.4784007668495178
+367,1,367,1156676417,LevoParadoxPPOHybrid,-2.0,overconfident,0.4788035750389099
+368,1,368,988215448,LevoParadoxPPOHybrid,1.3,neutral,0.4802131950855255
+369,1,369,1630817173,LevoParadoxPPOHybrid,-0.8,overcautious,0.48367613554000854
+370,1,370,1326405127,LevoParadoxPPOHybrid,1.3,neutral,0.4863224923610687
+371,1,371,279363521,LevoParadoxPPOHybrid,1.3,neutral,0.4805646538734436
+372,1,372,673386525,LevoParadoxPPOHybrid,0.0,neutral,0.479666531085968
+373,1,373,1485190259,LevoParadoxPPOHybrid,1.3,neutral,0.4808143377304077
+374,1,374,757249346,LevoParadoxPPOHybrid,1.3,neutral,0.4784007668495178
+375,1,375,2139173341,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+376,1,376,1407830714,LevoParadoxPPOHybrid,0.0,neutral,0.4777975380420685
+377,1,377,1328293493,LevoParadoxPPOHybrid,-2.0,overconfident,0.4783268868923187
+378,1,378,1561225140,LevoParadoxPPOHybrid,0.0,neutral,0.4802131950855255
+379,1,379,2054053838,LevoParadoxPPOHybrid,0.0,neutral,0.4791228175163269
+380,1,380,1538665569,LevoParadoxPPOHybrid,0.0,neutral,0.4773579239845276
+381,1,381,578245576,LevoParadoxPPOHybrid,1.3,neutral,0.4802131950855255
+382,1,382,1001750142,LevoParadoxPPOHybrid,1.3,neutral,0.4863224923610687
+383,1,383,247349919,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+384,1,384,1913641746,LevoParadoxPPOHybrid,0.0,neutral,0.48010820150375366
+385,1,385,191896624,LevoParadoxPPOHybrid,-0.8,overcautious,0.4782043993473053
+386,1,386,358450787,LevoParadoxPPOHybrid,0.0,neutral,0.47729626297950745
+387,1,387,1304006934,LevoParadoxPPOHybrid,1.3,neutral,0.4863224923610687
+388,1,388,854476003,LevoParadoxPPOHybrid,1.3,neutral,0.48244643211364746
+389,1,389,718810870,LevoParadoxPPOHybrid,0.0,neutral,0.48244643211364746
+390,1,390,2087848673,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+391,1,391,565983639,LevoParadoxPPOHybrid,0.0,neutral,0.48367613554000854
+392,1,392,406083697,LevoParadoxPPOHybrid,0.0,neutral,0.4805646538734436
+393,1,393,1812367355,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+394,1,394,1054994518,LevoParadoxPPOHybrid,1.3,neutral,0.4802131950855255
+395,1,395,235136936,LevoParadoxPPOHybrid,0.0,neutral,0.4808143377304077
+396,1,396,111732250,LevoParadoxPPOHybrid,-2.0,overconfident,0.4773579239845276
+397,1,397,289408596,LevoParadoxPPOHybrid,1.3,neutral,0.4802131950855255
+398,1,398,1197540953,LevoParadoxPPOHybrid,0.0,neutral,0.479666531085968
+399,1,399,34196977,LevoParadoxPPOHybrid,0.0,neutral,0.4818275570869446
+400,2,0,2034269964,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+401,2,1,912146831,LevoParadoxPPOHybrid,0.0,neutral,0.48142847418785095
+402,2,2,144856919,LevoParadoxPPOHybrid,1.2,neutral,0.47782430052757263
+403,2,3,685506823,LevoParadoxPPOHybrid,1.2,neutral,0.4803639352321625
+404,2,4,1939181862,LevoParadoxPPOHybrid,0.0,neutral,0.4791390895843506
+405,2,5,1502803201,LevoParadoxPPOHybrid,0.0,neutral,0.47879713773727417
+406,2,6,1504803878,LevoParadoxPPOHybrid,-3.0,overconfident,0.4803639352321625
+407,2,7,67883367,LevoParadoxPPOHybrid,2.0,neutral,0.48142847418785095
+408,2,8,235174993,LevoParadoxPPOHybrid,0.8,neutral,0.48121845722198486
+409,2,9,1767017071,LevoParadoxPPOHybrid,2.0,neutral,0.48142847418785095
+410,2,10,1195637117,LevoParadoxPPOHybrid,-3.0,overconfident,0.48439839482307434
+411,2,11,2140388521,LevoParadoxPPOHybrid,0.0,neutral,0.47790008783340454
+412,2,12,285128217,LevoParadoxPPOHybrid,0.0,neutral,0.47782430052757263
+413,2,13,1297214282,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+414,2,14,2128926662,LevoParadoxPPOHybrid,0.8,neutral,0.4789300858974457
+415,2,15,1227615478,LevoParadoxPPOHybrid,0.0,neutral,0.4791390895843506
+416,2,16,562820587,LevoParadoxPPOHybrid,-3.0,overconfident,0.48439839482307434
+417,2,17,1938965903,LevoParadoxPPOHybrid,2.0,neutral,0.47879713773727417
+418,2,18,1938899583,LevoParadoxPPOHybrid,0.0,neutral,0.48121845722198486
+419,2,19,146416322,LevoParadoxPPOHybrid,2.0,neutral,0.47879713773727417
+420,2,20,2034761172,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+421,2,21,1531184118,LevoParadoxPPOHybrid,0.8,neutral,0.47790008783340454
+422,2,22,1624823478,LevoParadoxPPOHybrid,-3.0,overconfident,0.47782430052757263
+423,2,23,1973982599,LevoParadoxPPOHybrid,0.0,neutral,0.47879713773727417
+424,2,24,1433121745,LevoParadoxPPOHybrid,0.0,neutral,0.47766703367233276
+425,2,25,269942302,LevoParadoxPPOHybrid,-0.8,overcautious,0.47879713773727417
+426,2,26,662577618,LevoParadoxPPOHybrid,-3.0,overconfident,0.48439839482307434
+427,2,27,1680792863,LevoParadoxPPOHybrid,1.2,neutral,0.47782430052757263
+428,2,28,608196857,LevoParadoxPPOHybrid,0.0,neutral,0.4791390895843506
+429,2,29,750797946,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+430,2,30,1703067462,LevoParadoxPPOHybrid,0.0,neutral,0.4789300858974457
+431,2,31,354277540,LevoParadoxPPOHybrid,2.0,neutral,0.48142847418785095
+432,2,32,516686757,LevoParadoxPPOHybrid,0.0,neutral,0.4789300858974457
+433,2,33,145581260,LevoParadoxPPOHybrid,0.8,neutral,0.47702041268348694
+434,2,34,1801241574,LevoParadoxPPOHybrid,2.0,neutral,0.47879713773727417
+435,2,35,2100309440,LevoParadoxPPOHybrid,2.0,neutral,0.47879713773727417
+436,2,36,1637824222,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+437,2,37,1781578254,LevoParadoxPPOHybrid,0.0,neutral,0.47879713773727417
+438,2,38,774535340,LevoParadoxPPOHybrid,0.0,neutral,0.47766703367233276
+439,2,39,1592536280,LevoParadoxPPOHybrid,0.8,neutral,0.47760096192359924
+440,2,40,803715272,LevoParadoxPPOHybrid,0.0,neutral,0.47968432307243347
+441,2,41,1295445709,LevoParadoxPPOHybrid,-0.8,overcautious,0.47879713773727417
+442,2,42,580455628,LevoParadoxPPOHybrid,0.8,neutral,0.47702041268348694
+443,2,43,608571353,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+444,2,44,220247617,LevoParadoxPPOHybrid,0.8,neutral,0.47702041268348694
+445,2,45,1007502304,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+446,2,46,1485027697,LevoParadoxPPOHybrid,1.2,neutral,0.47782430052757263
+447,2,47,308424123,LevoParadoxPPOHybrid,0.8,neutral,0.481337308883667
+448,2,48,1831539103,LevoParadoxPPOHybrid,0.0,neutral,0.4791390895843506
+449,2,49,96205481,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+450,2,50,572983894,LevoParadoxPPOHybrid,0.8,neutral,0.4789300858974457
+451,2,51,1559559556,LevoParadoxPPOHybrid,0.0,neutral,0.48121845722198486
+452,2,52,289588363,LevoParadoxPPOHybrid,0.0,neutral,0.4781184792518616
+453,2,53,1021080331,LevoParadoxPPOHybrid,0.8,neutral,0.47968432307243347
+454,2,54,43845046,LevoParadoxPPOHybrid,0.0,neutral,0.47968432307243347
+455,2,55,1969391451,LevoParadoxPPOHybrid,0.0,neutral,0.47984620928764343
+456,2,56,112141255,LevoParadoxPPOHybrid,0.0,neutral,0.47984620928764343
+457,2,57,407044235,LevoParadoxPPOHybrid,0.0,neutral,0.48121845722198486
+458,2,58,1413331621,LevoParadoxPPOHybrid,0.0,neutral,0.4791390895843506
+459,2,59,1124438664,LevoParadoxPPOHybrid,2.0,neutral,0.479806512594223
+460,2,60,1823383091,LevoParadoxPPOHybrid,0.0,neutral,0.47702041268348694
+461,2,61,1452420947,LevoParadoxPPOHybrid,-3.0,overconfident,0.47782430052757263
+462,2,62,2031250630,LevoParadoxPPOHybrid,-3.0,overconfident,0.4803639352321625
+463,2,63,788674418,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+464,2,64,1437390327,LevoParadoxPPOHybrid,0.8,neutral,0.48121845722198486
+465,2,65,879262169,LevoParadoxPPOHybrid,0.0,neutral,0.479806512594223
+466,2,66,958652521,LevoParadoxPPOHybrid,0.8,neutral,0.4793781638145447
+467,2,67,2142602411,LevoParadoxPPOHybrid,0.0,neutral,0.4789300858974457
+468,2,68,59947504,LevoParadoxPPOHybrid,0.8,neutral,0.47766703367233276
+469,2,69,384107305,LevoParadoxPPOHybrid,0.0,neutral,0.47968432307243347
+470,2,70,1830507375,LevoParadoxPPOHybrid,0.8,neutral,0.4789300858974457
+471,2,71,571519169,LevoParadoxPPOHybrid,0.8,neutral,0.4793781638145447
+472,2,72,1626492832,LevoParadoxPPOHybrid,0.8,neutral,0.4789300858974457
+473,2,73,1386853771,LevoParadoxPPOHybrid,0.8,neutral,0.47760096192359924
+474,2,74,1706685627,LevoParadoxPPOHybrid,-0.8,overcautious,0.47879713773727417
+475,2,75,1611897310,LevoParadoxPPOHybrid,2.0,neutral,0.48142847418785095
+476,2,76,1751003019,LevoParadoxPPOHybrid,0.8,neutral,0.47790008783340454
+477,2,77,1422082962,LevoParadoxPPOHybrid,1.2,neutral,0.48439839482307434
+478,2,78,1883691922,LevoParadoxPPOHybrid,0.0,neutral,0.481337308883667
+479,2,79,1013721795,LevoParadoxPPOHybrid,0.8,neutral,0.4791390895843506
+480,2,80,820766304,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+481,2,81,292577686,LevoParadoxPPOHybrid,0.8,neutral,0.481337308883667
+482,2,82,1774181236,LevoParadoxPPOHybrid,0.8,neutral,0.47968432307243347
+483,2,83,16477750,LevoParadoxPPOHybrid,0.8,neutral,0.4781184792518616
+484,2,84,270709347,LevoParadoxPPOHybrid,0.0,neutral,0.47766703367233276
+485,2,85,117649210,LevoParadoxPPOHybrid,0.8,neutral,0.47790008783340454
+486,2,86,1315442558,LevoParadoxPPOHybrid,0.0,neutral,0.47984620928764343
+487,2,87,2057676732,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+488,2,88,211459733,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+489,2,89,91146202,LevoParadoxPPOHybrid,0.0,neutral,0.47702041268348694
+490,2,90,588068454,LevoParadoxPPOHybrid,0.8,neutral,0.47968432307243347
+491,2,91,467526601,LevoParadoxPPOHybrid,-0.8,overcautious,0.479806512594223
+492,2,92,335299668,LevoParadoxPPOHybrid,0.8,neutral,0.47984620928764343
+493,2,93,779362037,LevoParadoxPPOHybrid,0.0,neutral,0.47968432307243347
+494,2,94,660339976,LevoParadoxPPOHybrid,2.0,neutral,0.479806512594223
+495,2,95,637136818,LevoParadoxPPOHybrid,1.2,neutral,0.48439839482307434
+496,2,96,1999866855,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+497,2,97,139896496,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+498,2,98,863743810,LevoParadoxPPOHybrid,0.0,neutral,0.4781184792518616
+499,2,99,1908673249,LevoParadoxPPOHybrid,2.0,neutral,0.479806512594223
+500,2,100,1039141574,LevoParadoxPPOHybrid,0.0,neutral,0.47984620928764343
+501,2,101,1051567495,LevoParadoxPPOHybrid,-3.0,overconfident,0.4803639352321625
+502,2,102,837394817,LevoParadoxPPOHybrid,0.8,neutral,0.47968432307243347
+503,2,103,2013481887,LevoParadoxPPOHybrid,0.0,neutral,0.47760096192359924
+504,2,104,438355621,LevoParadoxPPOHybrid,0.0,neutral,0.481337308883667
+505,2,105,993080718,LevoParadoxPPOHybrid,0.0,neutral,0.4781184792518616
+506,2,106,1682379032,LevoParadoxPPOHybrid,0.0,neutral,0.47766703367233276
+507,2,107,1013432362,LevoParadoxPPOHybrid,0.8,neutral,0.47702041268348694
+508,2,108,1856562955,LevoParadoxPPOHybrid,0.8,neutral,0.47702041268348694
+509,2,109,1667805176,LevoParadoxPPOHybrid,1.2,neutral,0.48439839482307434
+510,2,110,506300911,LevoParadoxPPOHybrid,0.0,neutral,0.4781184792518616
+511,2,111,222604131,LevoParadoxPPOHybrid,0.0,neutral,0.4793781638145447
+512,2,112,804599567,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+513,2,113,1492613445,LevoParadoxPPOHybrid,0.0,neutral,0.481283962726593
+514,2,114,552650077,LevoParadoxPPOHybrid,-3.0,overconfident,0.4819849133491516
+515,2,115,930025102,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+516,2,116,873459449,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+517,2,117,467662889,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+518,2,118,445679122,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+519,2,119,1328599419,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+520,2,120,1323806708,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+521,2,121,1034589592,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+522,2,122,413811598,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+523,2,123,753550936,LevoParadoxPPOHybrid,0.0,neutral,0.4846201539039612
+524,2,124,1202521986,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+525,2,125,1933381577,LevoParadoxPPOHybrid,0.0,neutral,0.4850122034549713
+526,2,126,1963329040,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+527,2,127,1399821071,LevoParadoxPPOHybrid,0.0,neutral,0.4850122034549713
+528,2,128,1828929971,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+529,2,129,1100686433,LevoParadoxPPOHybrid,0.0,neutral,0.4818302094936371
+530,2,130,423774818,LevoParadoxPPOHybrid,0.8,neutral,0.4854736030101776
+531,2,131,2047001195,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+532,2,132,127060851,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+533,2,133,1430942851,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+534,2,134,1194133788,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+535,2,135,30246456,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+536,2,136,1871301169,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+537,2,137,1724977905,LevoParadoxPPOHybrid,0.0,neutral,0.4846201539039612
+538,2,138,1717796771,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+539,2,139,1593858573,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+540,2,140,200663940,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+541,2,141,1366649604,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+542,2,142,1673519505,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+543,2,143,1177626327,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+544,2,144,262855495,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+545,2,145,1035690989,LevoParadoxPPOHybrid,0.0,neutral,0.4819849133491516
+546,2,146,192999926,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+547,2,147,1097961901,LevoParadoxPPOHybrid,-0.8,overcautious,0.48265746235847473
+548,2,148,1040646467,LevoParadoxPPOHybrid,2.0,neutral,0.4850122034549713
+549,2,149,1995566658,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+550,2,150,390674833,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+551,2,151,662645968,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+552,2,152,111224037,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+553,2,153,1242319339,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+554,2,154,1295579201,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+555,2,155,88079789,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+556,2,156,1218832160,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+557,2,157,1199450774,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+558,2,158,93920744,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+559,2,159,301211568,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+560,2,160,606831608,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+561,2,161,248267762,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+562,2,162,479831997,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+563,2,163,443794762,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+564,2,164,317845088,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+565,2,165,976368810,LevoParadoxPPOHybrid,0.0,neutral,0.4876437187194824
+566,2,166,1096060710,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+567,2,167,1786432503,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+568,2,168,92915493,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+569,2,169,169421372,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+570,2,170,827738447,LevoParadoxPPOHybrid,-0.8,overcautious,0.48265746235847473
+571,2,171,91871503,LevoParadoxPPOHybrid,0.8,neutral,0.4812925159931183
+572,2,172,1181786707,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+573,2,173,14354338,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+574,2,174,2140443672,LevoParadoxPPOHybrid,0.0,neutral,0.4819849133491516
+575,2,175,318790932,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+576,2,176,721850175,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+577,2,177,1030785601,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+578,2,178,1316251630,LevoParadoxPPOHybrid,0.0,neutral,0.4850122034549713
+579,2,179,1719447051,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+580,2,180,574594100,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+581,2,181,2130613154,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+582,2,182,527701015,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+583,2,183,1227208042,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+584,2,184,633972706,LevoParadoxPPOHybrid,0.8,neutral,0.4818302094936371
+585,2,185,1481264686,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+586,2,186,1403332419,LevoParadoxPPOHybrid,0.8,neutral,0.48329493403434753
+587,2,187,573328882,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+588,2,188,896430961,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+589,2,189,861308429,LevoParadoxPPOHybrid,-3.0,overconfident,0.4819849133491516
+590,2,190,857542805,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+591,2,191,555627012,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+592,2,192,883473089,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+593,2,193,1183906577,LevoParadoxPPOHybrid,-0.8,overcautious,0.48265746235847473
+594,2,194,1295404722,LevoParadoxPPOHybrid,0.0,neutral,0.4842311441898346
+595,2,195,1602782174,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+596,2,196,1168170146,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+597,2,197,247049848,LevoParadoxPPOHybrid,0.8,neutral,0.4829106330871582
+598,2,198,1217521579,LevoParadoxPPOHybrid,2.0,neutral,0.4850122034549713
+599,2,199,1154221074,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+600,2,200,1039822287,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+601,2,201,403774715,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+602,2,202,1937799744,LevoParadoxPPOHybrid,0.0,neutral,0.4812925159931183
+603,2,203,1530207800,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+604,2,204,899360822,LevoParadoxPPOHybrid,0.8,neutral,0.4812925159931183
+605,2,205,839140609,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+606,2,206,2141035731,LevoParadoxPPOHybrid,0.0,neutral,0.4842311441898346
+607,2,207,778084307,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+608,2,208,350945807,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+609,2,209,970046052,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+610,2,210,828658713,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+611,2,211,899193899,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+612,2,212,1918263815,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+613,2,213,861402774,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+614,2,214,545630730,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+615,2,215,1771344806,LevoParadoxPPOHybrid,0.8,neutral,0.4818302094936371
+616,2,216,835556689,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+617,2,217,920739262,LevoParadoxPPOHybrid,0.8,neutral,0.4857918918132782
+618,2,218,1075133249,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+619,2,219,1466130915,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+620,2,220,1545638251,LevoParadoxPPOHybrid,-3.0,overconfident,0.4819849133491516
+621,2,221,379504837,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+622,2,222,833504022,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+623,2,223,1846676001,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+624,2,224,6416948,LevoParadoxPPOHybrid,0.0,neutral,0.4818302094936371
+625,2,225,1751117666,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+626,2,226,284570123,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+627,2,227,189352376,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+628,2,228,1370043294,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+629,2,229,1967776583,LevoParadoxPPOHybrid,0.0,neutral,0.4818302094936371
+630,2,230,1013942334,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+631,2,231,600184202,LevoParadoxPPOHybrid,0.8,neutral,0.4829106330871582
+632,2,232,2085636407,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+633,2,233,328848547,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+634,2,234,63425983,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+635,2,235,531264304,LevoParadoxPPOHybrid,0.0,neutral,0.48265746235847473
+636,2,236,1531168957,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+637,2,237,1789312949,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+638,2,238,1084402395,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+639,2,239,1545622175,LevoParadoxPPOHybrid,0.8,neutral,0.4854736030101776
+640,2,240,999431888,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+641,2,241,1725426490,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+642,2,242,669407095,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+643,2,243,1417993163,LevoParadoxPPOHybrid,0.8,neutral,0.4818302094936371
+644,2,244,1057045593,LevoParadoxPPOHybrid,0.8,neutral,0.4829106330871582
+645,2,245,1380890934,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+646,2,246,238129855,LevoParadoxPPOHybrid,0.8,neutral,0.4854736030101776
+647,2,247,1486485713,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+648,2,248,30716145,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+649,2,249,1615261593,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+650,2,250,2052819092,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+651,2,251,11185487,LevoParadoxPPOHybrid,0.0,neutral,0.4812925159931183
+652,2,252,928000143,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+653,2,253,2023963770,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+654,2,254,1301736393,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+655,2,255,398352915,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+656,2,256,299104752,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+657,2,257,1900619662,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+658,2,258,402834001,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+659,2,259,618191185,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+660,2,260,1107226955,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+661,2,261,1079996997,LevoParadoxPPOHybrid,0.8,neutral,0.4857918918132782
+662,2,262,1090799708,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+663,2,263,614807561,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+664,2,264,1110540374,LevoParadoxPPOHybrid,0.0,neutral,0.4850122034549713
+665,2,265,230358128,LevoParadoxPPOHybrid,0.8,neutral,0.4857918918132782
+666,2,266,1307025172,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+667,2,267,2128513208,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+668,2,268,1813190214,LevoParadoxPPOHybrid,1.2,neutral,0.4876437187194824
+669,2,269,427517589,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+670,2,270,971704768,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+671,2,271,379120844,LevoParadoxPPOHybrid,0.8,neutral,0.4857918918132782
+672,2,272,366674027,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+673,2,273,1267589757,LevoParadoxPPOHybrid,0.8,neutral,0.4818302094936371
+674,2,274,397609752,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+675,2,275,544583423,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+676,2,276,1765101426,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+677,2,277,569693597,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+678,2,278,189460983,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+679,2,279,505940532,LevoParadoxPPOHybrid,0.0,neutral,0.481283962726593
+680,2,280,961700536,LevoParadoxPPOHybrid,1.2,neutral,0.4819849133491516
+681,2,281,932456279,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+682,2,282,390287427,LevoParadoxPPOHybrid,2.0,neutral,0.4850122034549713
+683,2,283,297976648,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+684,2,284,436098708,LevoParadoxPPOHybrid,0.0,neutral,0.481283962726593
+685,2,285,794629335,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+686,2,286,1214082779,LevoParadoxPPOHybrid,0.8,neutral,0.481283962726593
+687,2,287,1935214602,LevoParadoxPPOHybrid,0.0,neutral,0.4842311441898346
+688,2,288,284248471,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+689,2,289,1230641234,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+690,2,290,388858966,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+691,2,291,1021791854,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+692,2,292,603303402,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+693,2,293,862608619,LevoParadoxPPOHybrid,0.8,neutral,0.481283962726593
+694,2,294,1651871421,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+695,2,295,2051444313,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+696,2,296,144628230,LevoParadoxPPOHybrid,0.8,neutral,0.48329493403434753
+697,2,297,1520429634,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+698,2,298,881109934,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+699,2,299,1255956995,LevoParadoxPPOHybrid,0.0,neutral,0.481283962726593
+700,2,300,208230768,LevoParadoxPPOHybrid,0.8,neutral,0.4829106330871582
+701,2,301,1460257335,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+702,2,302,1011782590,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+703,2,303,1789340557,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+704,2,304,1869217819,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+705,2,305,1673802110,LevoParadoxPPOHybrid,0.0,neutral,0.481283962726593
+706,2,306,62463393,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+707,2,307,2074841997,LevoParadoxPPOHybrid,-3.0,overconfident,0.4846201539039612
+708,2,308,1764116324,LevoParadoxPPOHybrid,-3.0,overconfident,0.4819849133491516
+709,2,309,1361763883,LevoParadoxPPOHybrid,0.0,neutral,0.4819849133491516
+710,2,310,2068149571,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+711,2,311,1761270974,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+712,2,312,1610445955,LevoParadoxPPOHybrid,0.8,neutral,0.4818302094936371
+713,2,313,527574022,LevoParadoxPPOHybrid,0.8,neutral,0.4857918918132782
+714,2,314,1948416385,LevoParadoxPPOHybrid,0.8,neutral,0.4812925159931183
+715,2,315,842515162,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+716,2,316,728127582,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+717,2,317,1807809684,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+718,2,318,1911608906,LevoParadoxPPOHybrid,0.8,neutral,0.481283962726593
+719,2,319,507860031,LevoParadoxPPOHybrid,0.8,neutral,0.48329493403434753
+720,2,320,1559660249,LevoParadoxPPOHybrid,0.0,neutral,0.4812925159931183
+721,2,321,817113483,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+722,2,322,181612330,LevoParadoxPPOHybrid,0.0,neutral,0.4818302094936371
+723,2,323,330146939,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+724,2,324,1451519916,LevoParadoxPPOHybrid,0.8,neutral,0.48329493403434753
+725,2,325,429715005,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+726,2,326,1836744305,LevoParadoxPPOHybrid,2.0,neutral,0.48265746235847473
+727,2,327,200999922,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+728,2,328,686960989,LevoParadoxPPOHybrid,1.2,neutral,0.4819849133491516
+729,2,329,2137759524,LevoParadoxPPOHybrid,0.0,neutral,0.4857918918132782
+730,2,330,48470119,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+731,2,331,1429722682,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+732,2,332,218074447,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+733,2,333,2104545774,LevoParadoxPPOHybrid,0.8,neutral,0.4811937212944031
+734,2,334,1816453390,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+735,2,335,639577193,LevoParadoxPPOHybrid,0.0,neutral,0.48379752039909363
+736,2,336,231860645,LevoParadoxPPOHybrid,1.2,neutral,0.4846201539039612
+737,2,337,1142197128,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+738,2,338,1321680746,LevoParadoxPPOHybrid,0.8,neutral,0.48379752039909363
+739,2,339,1746554905,LevoParadoxPPOHybrid,0.0,neutral,0.4812925159931183
+740,2,340,396707811,LevoParadoxPPOHybrid,0.8,neutral,0.4854736030101776
+741,2,341,491514850,LevoParadoxPPOHybrid,0.8,neutral,0.48286885023117065
+742,2,342,461674718,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+743,2,343,1701500925,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+744,2,344,1344298913,LevoParadoxPPOHybrid,0.0,neutral,0.4818302094936371
+745,2,345,1354823084,LevoParadoxPPOHybrid,0.0,neutral,0.4811937212944031
+746,2,346,118160105,LevoParadoxPPOHybrid,1.2,neutral,0.4819849133491516
+747,2,347,1044584312,LevoParadoxPPOHybrid,0.8,neutral,0.4839756190776825
+748,2,348,1524392230,LevoParadoxPPOHybrid,0.0,neutral,0.4819849133491516
+749,2,349,428869864,LevoParadoxPPOHybrid,0.8,neutral,0.48329493403434753
+750,2,350,180994417,LevoParadoxPPOHybrid,0.8,neutral,0.4829106330871582
+751,2,351,885019589,LevoParadoxPPOHybrid,0.0,neutral,0.4812925159931183
+752,2,352,1231928222,LevoParadoxPPOHybrid,0.0,neutral,0.4854736030101776
+753,2,353,1670237802,LevoParadoxPPOHybrid,0.0,neutral,0.4839756190776825
+754,2,354,1414482049,LevoParadoxPPOHybrid,0.8,neutral,0.4854736030101776
+755,2,355,1971477631,LevoParadoxPPOHybrid,2.0,neutral,0.4842311441898346
+756,2,356,883215094,LevoParadoxPPOHybrid,0.0,neutral,0.4846201539039612
+757,2,357,343285816,LevoParadoxPPOHybrid,-3.0,overconfident,0.4819849133491516
+758,2,358,1993664325,LevoParadoxPPOHybrid,-0.8,overcautious,0.4850122034549713
+759,2,359,538994041,LevoParadoxPPOHybrid,0.8,neutral,0.4818005859851837
+760,2,360,365720817,LevoParadoxPPOHybrid,-0.8,overcautious,0.4842311441898346
+761,2,361,1931073180,LevoParadoxPPOHybrid,0.0,neutral,0.48286885023117065
+762,2,362,330654991,LevoParadoxPPOHybrid,0.0,neutral,0.4818005859851837
+763,2,363,2117986561,LevoParadoxPPOHybrid,-3.0,overconfident,0.4876437187194824
+764,2,364,1745610389,LevoParadoxPPOHybrid,0.0,neutral,0.48265746235847473
+765,2,365,1935491097,LevoParadoxPPOHybrid,0.8,neutral,0.481283962726593
+766,2,366,816460352,LevoParadoxPPOHybrid,0.0,neutral,0.48329493403434753
+767,2,367,2034287239,LevoParadoxPPOHybrid,0.0,neutral,0.4829106330871582
+768,2,368,1494357375,LevoParadoxPPOHybrid,0.8,neutral,0.48519960045814514
+769,2,369,525099930,LevoParadoxPPOHybrid,0.8,neutral,0.48731231689453125
+770,2,370,461861420,LevoParadoxPPOHybrid,0.8,neutral,0.4886108636856079
+771,2,371,424434369,LevoParadoxPPOHybrid,0.0,neutral,0.48417460918426514
+772,2,372,1696040440,LevoParadoxPPOHybrid,0.0,neutral,0.48826003074645996
+773,2,373,1327732050,LevoParadoxPPOHybrid,0.8,neutral,0.48468780517578125
+774,2,374,1275639253,LevoParadoxPPOHybrid,0.8,neutral,0.48519960045814514
+775,2,375,180175624,LevoParadoxPPOHybrid,0.0,neutral,0.48731231689453125
+776,2,376,853325992,LevoParadoxPPOHybrid,1.2,neutral,0.48919999599456787
+777,2,377,35314844,LevoParadoxPPOHybrid,0.0,neutral,0.4842897355556488
+778,2,378,512421119,LevoParadoxPPOHybrid,-0.8,overcautious,0.48792359232902527
+779,2,379,1818877815,LevoParadoxPPOHybrid,0.0,neutral,0.4842897355556488
+780,2,380,244612068,LevoParadoxPPOHybrid,0.8,neutral,0.48738181591033936
+781,2,381,438090890,LevoParadoxPPOHybrid,0.8,neutral,0.4872293472290039
+782,2,382,71908212,LevoParadoxPPOHybrid,0.0,neutral,0.4857252836227417
+783,2,383,763979621,LevoParadoxPPOHybrid,0.0,neutral,0.4857252836227417
+784,2,384,492409950,LevoParadoxPPOHybrid,0.8,neutral,0.4872293472290039
+785,2,385,1939330706,LevoParadoxPPOHybrid,-3.0,overconfident,0.48516950011253357
+786,2,386,304266562,LevoParadoxPPOHybrid,0.8,neutral,0.4857252836227417
+787,2,387,1839152413,LevoParadoxPPOHybrid,0.8,neutral,0.48519960045814514
+788,2,388,1584964798,LevoParadoxPPOHybrid,0.8,neutral,0.48738181591033936
+789,2,389,1705345529,LevoParadoxPPOHybrid,0.0,neutral,0.48468780517578125
+790,2,390,530680927,LevoParadoxPPOHybrid,0.0,neutral,0.48826003074645996
+791,2,391,601158382,LevoParadoxPPOHybrid,-3.0,overconfident,0.4865123927593231
+792,2,392,661312537,LevoParadoxPPOHybrid,0.8,neutral,0.4886108636856079
+793,2,393,1099910893,LevoParadoxPPOHybrid,0.8,neutral,0.48417460918426514
+794,2,394,1777836792,LevoParadoxPPOHybrid,1.2,neutral,0.4865123927593231
+795,2,395,320819501,LevoParadoxPPOHybrid,0.0,neutral,0.48519960045814514
+796,2,396,1262930064,LevoParadoxPPOHybrid,0.0,neutral,0.48468780517578125
+797,2,397,1476382876,LevoParadoxPPOHybrid,0.8,neutral,0.48468780517578125
+798,2,398,229480971,LevoParadoxPPOHybrid,0.0,neutral,0.48731231689453125
+799,2,399,1346765522,LevoParadoxPPOHybrid,0.0,neutral,0.48731231689453125
+800,3,0,1015467875,LevoParadoxPPOHybrid,0.49029308438457947,neutral,0.48342037200927734
+801,3,1,871703027,LevoParadoxPPOHybrid,0.2583186351063763,neutral,0.48847508430480957
+802,3,2,1121193219,LevoParadoxPPOHybrid,-0.9285687801394484,overcautious,0.4896336793899536
+803,3,3,1844331605,LevoParadoxPPOHybrid,1.268712954493355,neutral,0.4856588840484619
+804,3,4,376821421,LevoParadoxPPOHybrid,-2.1540989698282287,overconfident,0.49011749029159546
+805,3,5,1528947293,LevoParadoxPPOHybrid,1.9236203197456223,neutral,0.49011749029159546
+806,3,6,1457385050,LevoParadoxPPOHybrid,-0.388878696540991,neutral,0.48847508430480957
+807,3,7,1930833205,LevoParadoxPPOHybrid,1.8947708652049988,neutral,0.4878310561180115
+808,3,8,331658631,LevoParadoxPPOHybrid,-0.34637226145235095,overcautious,0.48912549018859863
+809,3,9,1392760183,LevoParadoxPPOHybrid,-1.7470900488231007,overconfident,0.48763468861579895
+810,3,10,1410154195,LevoParadoxPPOHybrid,0.37503834948416703,neutral,0.48973292112350464
+811,3,11,1330897656,LevoParadoxPPOHybrid,1.9046013636486836,neutral,0.49011749029159546
+812,3,12,755426389,LevoParadoxPPOHybrid,-0.08449315822425026,neutral,0.4876002371311188
+813,3,13,478344337,LevoParadoxPPOHybrid,-0.20334804248604185,neutral,0.48973292112350464
+814,3,14,2135473267,LevoParadoxPPOHybrid,-0.321538017298501,neutral,0.48342037200927734
+815,3,15,1381920965,LevoParadoxPPOHybrid,-0.8946149359899849,neutral,0.4856588840484619
+816,3,16,790353978,LevoParadoxPPOHybrid,0.805089675891586,neutral,0.48261088132858276
+817,3,17,886917887,LevoParadoxPPOHybrid,-2.0434560465424223,overconfident,0.48763468861579895
+818,3,18,1326287162,LevoParadoxPPOHybrid,0.35056089697345333,neutral,0.4878310561180115
+819,3,19,132467973,LevoParadoxPPOHybrid,0.7640856467236494,neutral,0.49011749029159546
+820,3,20,896694920,LevoParadoxPPOHybrid,0.6314368917416728,neutral,0.48973292112350464
+821,3,21,265295982,LevoParadoxPPOHybrid,0.472286750236512,neutral,0.48261088132858276
+822,3,22,188123431,LevoParadoxPPOHybrid,0.7270211373649718,neutral,0.4841948449611664
+823,3,23,1385912200,LevoParadoxPPOHybrid,0.5742713840510624,neutral,0.48847508430480957
+824,3,24,449987729,LevoParadoxPPOHybrid,0.150792838879278,neutral,0.4853600561618805
+825,3,25,593302824,LevoParadoxPPOHybrid,-0.045015931355259255,neutral,0.48763468861579895
+826,3,26,194879931,LevoParadoxPPOHybrid,0.17815411284413574,neutral,0.4832690358161926
+827,3,27,1758518115,LevoParadoxPPOHybrid,1.045585673873838,neutral,0.49009424448013306
+828,3,28,1545311517,LevoParadoxPPOHybrid,0.1627856125489353,neutral,0.4856588840484619
+829,3,29,562603550,LevoParadoxPPOHybrid,1.2183364147364109,neutral,0.48261088132858276
+830,3,30,1059808449,LevoParadoxPPOHybrid,1.2917831804345576,neutral,0.48261088132858276
+831,3,31,614383066,LevoParadoxPPOHybrid,0.016342221885410586,neutral,0.48973292112350464
+832,3,32,1192512057,LevoParadoxPPOHybrid,-0.5442359769557571,overcautious,0.48912549018859863
+833,3,33,950581637,LevoParadoxPPOHybrid,-0.8977958223217277,neutral,0.48956099152565
+834,3,34,1863772308,LevoParadoxPPOHybrid,0.45308998140979106,neutral,0.48973292112350464
+835,3,35,1007304414,LevoParadoxPPOHybrid,0.5254077508874286,neutral,0.48956099152565
+836,3,36,809066309,LevoParadoxPPOHybrid,-1.1110986321443062,overcautious,0.4853600561618805
+837,3,37,1514006645,LevoParadoxPPOHybrid,-0.3418469151415457,neutral,0.48763468861579895
+838,3,38,1146495174,LevoParadoxPPOHybrid,-0.2942163006184566,neutral,0.48847508430480957
+839,3,39,1859928275,LevoParadoxPPOHybrid,2.5861061474664324,neutral,0.48912549018859863
+840,3,40,303035184,LevoParadoxPPOHybrid,2.2035140269583504,neutral,0.4896336793899536
+841,3,41,1724145893,LevoParadoxPPOHybrid,0.9938873972881753,neutral,0.4878310561180115
+842,3,42,1035035036,LevoParadoxPPOHybrid,2.053749158155912,neutral,0.4853600561618805
+843,3,43,278635683,LevoParadoxPPOHybrid,1.54230006707985,neutral,0.4896336793899536
+844,3,44,377801781,LevoParadoxPPOHybrid,2.2669636777741404,neutral,0.48912549018859863
+845,3,45,1487506785,LevoParadoxPPOHybrid,0.5730128127334602,neutral,0.4841948449611664
+846,3,46,766738768,LevoParadoxPPOHybrid,1.7451958636224911,neutral,0.48763468861579895
+847,3,47,1469590444,LevoParadoxPPOHybrid,-0.11980525970062122,neutral,0.4856588840484619
+848,3,48,1527511243,LevoParadoxPPOHybrid,0.47426746416333615,neutral,0.4882761538028717
+849,3,49,1889868992,LevoParadoxPPOHybrid,0.18740335053313484,neutral,0.48342037200927734
+850,3,50,808836466,LevoParadoxPPOHybrid,1.4424045317149554,neutral,0.48342037200927734
+851,3,51,880209119,LevoParadoxPPOHybrid,-0.41698242562591825,neutral,0.4856588840484619
+852,3,52,2069774123,LevoParadoxPPOHybrid,1.1358277140176187,neutral,0.48973292112350464
+853,3,53,59978537,LevoParadoxPPOHybrid,1.2273143926451668,neutral,0.48261088132858276
+854,3,54,268314216,LevoParadoxPPOHybrid,1.1038476892173612,neutral,0.49011749029159546
+855,3,55,786773386,LevoParadoxPPOHybrid,1.2293653945237177,neutral,0.48973292112350464
+856,3,56,1078804137,LevoParadoxPPOHybrid,1.9968823651873315,neutral,0.4896336793899536
+857,3,57,88529032,LevoParadoxPPOHybrid,0.9139455244229703,neutral,0.48261088132858276
+858,3,58,733437594,LevoParadoxPPOHybrid,0.9998437802015792,neutral,0.4832690358161926
+859,3,59,1895500654,LevoParadoxPPOHybrid,1.1147969815448564,neutral,0.4832690358161926
+860,3,60,1156794677,LevoParadoxPPOHybrid,0.2822839034147425,neutral,0.4878310561180115
+861,3,61,1733698348,LevoParadoxPPOHybrid,1.1083787111148988,neutral,0.48261088132858276
+862,3,62,1987643195,LevoParadoxPPOHybrid,1.6494336690825908,neutral,0.48973292112350464
+863,3,63,968829821,LevoParadoxPPOHybrid,-1.3638653475842197,overcautious,0.4896336793899536
+864,3,64,2125839782,LevoParadoxPPOHybrid,-0.2783466794080766,neutral,0.4832690358161926
+865,3,65,1588322590,LevoParadoxPPOHybrid,2.2909286951175467,neutral,0.4859134256839752
+866,3,66,956367808,LevoParadoxPPOHybrid,-0.13769164780464407,neutral,0.49011749029159546
+867,3,67,2135886715,LevoParadoxPPOHybrid,0.9020338710094363,neutral,0.48261088132858276
+868,3,68,705297723,LevoParadoxPPOHybrid,2.267886356355249,neutral,0.48912549018859863
+869,3,69,493712258,LevoParadoxPPOHybrid,0.07995221270930497,neutral,0.4876002371311188
+870,3,70,75420035,LevoParadoxPPOHybrid,1.2007933704297795,neutral,0.4856588840484619
+871,3,71,1867815399,LevoParadoxPPOHybrid,-1.475944225695721,overconfident,0.4882761538028717
+872,3,72,509860178,LevoParadoxPPOHybrid,-1.0158303213136595,neutral,0.48973292112350464
+873,3,73,687658415,LevoParadoxPPOHybrid,-0.044455013110146525,neutral,0.48956099152565
+874,3,74,319346835,LevoParadoxPPOHybrid,1.1336872360109915,neutral,0.49009424448013306
+875,3,75,985914893,LevoParadoxPPOHybrid,-1.1520970521006983,overconfident,0.49011749029159546
+876,3,76,456914363,LevoParadoxPPOHybrid,-0.5563823345326576,neutral,0.48847508430480957
+877,3,77,1903234235,LevoParadoxPPOHybrid,-0.6168972120738625,neutral,0.48973292112350464
+878,3,78,1104964239,LevoParadoxPPOHybrid,-0.5537494140426967,neutral,0.4859134256839752
+879,3,79,800272976,LevoParadoxPPOHybrid,1.0014376600959138,neutral,0.48763468861579895
+880,3,80,514077870,LevoParadoxPPOHybrid,1.348889466402683,neutral,0.4878310561180115
+881,3,81,2084198909,LevoParadoxPPOHybrid,1.1757541313429112,neutral,0.48973292112350464
+882,3,82,909365514,LevoParadoxPPOHybrid,1.6573741369785153,neutral,0.4832690358161926
+883,3,83,1338386269,LevoParadoxPPOHybrid,-2.0258053677846037,overconfident,0.4882761538028717
+884,3,84,947429886,LevoParadoxPPOHybrid,0.6408681653569512,neutral,0.48342037200927734
+885,3,85,58590834,LevoParadoxPPOHybrid,1.324978671615042,neutral,0.49009424448013306
+886,3,86,684669113,LevoParadoxPPOHybrid,0.7636384160065989,neutral,0.48973292112350464
+887,3,87,898917724,LevoParadoxPPOHybrid,-1.2982161592441008,overcautious,0.48912549018859863
+888,3,88,548630351,LevoParadoxPPOHybrid,-0.2270660769418316,neutral,0.4832690358161926
+889,3,89,1616758964,LevoParadoxPPOHybrid,1.9765640849812711,neutral,0.49011749029159546
+890,3,90,2082505679,LevoParadoxPPOHybrid,-0.6935378671763233,neutral,0.4841948449611664
+891,3,91,741581883,LevoParadoxPPOHybrid,-0.0517050103645994,neutral,0.48847508430480957
+892,3,92,393553070,LevoParadoxPPOHybrid,-1.66724885658832,overconfident,0.48763468861579895
+893,3,93,890404536,LevoParadoxPPOHybrid,0.11880035600180827,neutral,0.48847508430480957
+894,3,94,179225205,LevoParadoxPPOHybrid,1.1151171438000786,neutral,0.48912549018859863
+895,3,95,852483643,LevoParadoxPPOHybrid,1.0428840098478962,neutral,0.4878310561180115
+896,3,96,596549345,LevoParadoxPPOHybrid,1.0439902976490092,neutral,0.4859134256839752
+897,3,97,1246421089,LevoParadoxPPOHybrid,1.363801434612391,neutral,0.48763468861579895
+898,3,98,505921964,LevoParadoxPPOHybrid,-1.858527642939623,overconfident,0.49011749029159546
+899,3,99,1670483738,LevoParadoxPPOHybrid,2.368154360450977,neutral,0.4896336793899536
+900,3,100,598294997,LevoParadoxPPOHybrid,1.318673479308487,neutral,0.4878310561180115
+901,3,101,1370406670,LevoParadoxPPOHybrid,-0.6009999531995625,overcautious,0.48912549018859863
+902,3,102,1690182943,LevoParadoxPPOHybrid,0.4903581932462463,neutral,0.4882761538028717
+903,3,103,1763960416,LevoParadoxPPOHybrid,0.10068445791849981,neutral,0.4878310561180115
+904,3,104,1645746969,LevoParadoxPPOHybrid,0.4513871460018203,neutral,0.4882761538028717
+905,3,105,820931753,LevoParadoxPPOHybrid,0.962884542235,neutral,0.4878310561180115
+906,3,106,1937660724,LevoParadoxPPOHybrid,0.20491593125696084,neutral,0.48763468861579895
+907,3,107,536112434,LevoParadoxPPOHybrid,-0.6515974640687232,neutral,0.4859134256839752
+908,3,108,852564596,LevoParadoxPPOHybrid,1.9825755691164177,neutral,0.49011749029159546
+909,3,109,2109579034,LevoParadoxPPOHybrid,0.849483274776245,neutral,0.4876002371311188
+910,3,110,1119218651,LevoParadoxPPOHybrid,2.36103349569904,neutral,0.48912549018859863
+911,3,111,1171844126,LevoParadoxPPOHybrid,0.6209249958307298,neutral,0.4896336793899536
+912,3,112,1066832,LevoParadoxPPOHybrid,-1.592546479482495,overconfident,0.48763468861579895
+913,3,113,2039855300,LevoParadoxPPOHybrid,-1.504881615380498,overconfident,0.49011749029159546
+914,3,114,1830413706,LevoParadoxPPOHybrid,0.6645794180980706,neutral,0.48973292112350464
+915,3,115,1536434038,LevoParadoxPPOHybrid,-2.4062797861421963,overconfident,0.48763468861579895
+916,3,116,204181968,LevoParadoxPPOHybrid,0.6050677080581792,neutral,0.4841948449611664
+917,3,117,782074719,LevoParadoxPPOHybrid,0.5091854411977584,neutral,0.48342037200927734
+918,3,118,1683677695,LevoParadoxPPOHybrid,-0.13301843465527774,neutral,0.4882761538028717
+919,3,119,811003462,LevoParadoxPPOHybrid,0.23188393912158678,neutral,0.48973292112350464
+920,3,120,1843531798,LevoParadoxPPOHybrid,-2.1411527352505653,overconfident,0.4882761538028717
+921,3,121,244956048,LevoParadoxPPOHybrid,-3.0,overconfident,0.4882761538028717
+922,3,122,521744775,LevoParadoxPPOHybrid,1.2719310359187297,neutral,0.48973292112350464
+923,3,123,137476059,LevoParadoxPPOHybrid,-0.01526260625040965,overcautious,0.4896336793899536
+924,3,124,1493249395,LevoParadoxPPOHybrid,0.7601033690157201,neutral,0.48342037200927734
+925,3,125,1658377656,LevoParadoxPPOHybrid,-0.0057138258475040145,neutral,0.48847508430480957
+926,3,126,1598149764,LevoParadoxPPOHybrid,-0.7555320294233012,overcautious,0.4896336793899536
+927,3,127,1973871594,LevoParadoxPPOHybrid,-0.17183341968445853,neutral,0.48342037200927734
+928,3,128,1575988774,LevoParadoxPPOHybrid,1.1486204800254285,neutral,0.4878310561180115
+929,3,129,352160276,LevoParadoxPPOHybrid,-0.09552234063130116,overcautious,0.4896336793899536
+930,3,130,1951098323,LevoParadoxPPOHybrid,-0.30943551223890026,overcautious,0.4896336793899536
+931,3,131,496971788,LevoParadoxPPOHybrid,0.12521537161674298,neutral,0.48342037200927734
+932,3,132,1527624590,LevoParadoxPPOHybrid,0.27854327915885413,neutral,0.4859134256839752
+933,3,133,400244675,LevoParadoxPPOHybrid,0.4690883568339111,neutral,0.48261088132858276
+934,3,134,386029926,LevoParadoxPPOHybrid,1.6115419185654172,neutral,0.48847508430480957
+935,3,135,573115058,LevoParadoxPPOHybrid,0.3348870252362766,neutral,0.4878310561180115
+936,3,136,575530726,LevoParadoxPPOHybrid,-0.4869844334164761,neutral,0.4878310561180115
+937,3,137,1964806113,LevoParadoxPPOHybrid,1.646603023563101,neutral,0.48956099152565
+938,3,138,1773149752,LevoParadoxPPOHybrid,-1.4756296842408974,overcautious,0.4853600561618805
+939,3,139,1241865463,LevoParadoxPPOHybrid,0.7525970473504748,neutral,0.4856588840484619
+940,3,140,330338515,LevoParadoxPPOHybrid,0.14435952462764418,neutral,0.49011749029159546
+941,3,141,761423743,LevoParadoxPPOHybrid,1.498481325424641,neutral,0.48912549018859863
+942,3,142,455706626,LevoParadoxPPOHybrid,-0.4525828220379003,neutral,0.48342037200927734
+943,3,143,1509528211,LevoParadoxPPOHybrid,0.26559561736173787,neutral,0.48847508430480957
+944,3,144,1356675324,LevoParadoxPPOHybrid,-3.0,overconfident,0.48763468861579895
+945,3,145,567724229,LevoParadoxPPOHybrid,-1.9704376301344195,overconfident,0.48763468861579895
+946,3,146,490459625,LevoParadoxPPOHybrid,-0.4252469944816251,neutral,0.48973292112350464
+947,3,147,1407038082,LevoParadoxPPOHybrid,-0.4074473579795859,neutral,0.4832690358161926
+948,3,148,247701853,LevoParadoxPPOHybrid,0.8320849787931718,neutral,0.48261088132858276
+949,3,149,1656318998,LevoParadoxPPOHybrid,-1.844959510594005,overconfident,0.49011749029159546
+950,3,150,1050418889,LevoParadoxPPOHybrid,-1.712401117846471,overconfident,0.4882761538028717
+951,3,151,1647828121,LevoParadoxPPOHybrid,-2.585520438420843,overconfident,0.49011749029159546
+952,3,152,1665251223,LevoParadoxPPOHybrid,0.4598923759613715,neutral,0.48956099152565
+953,3,153,1158119200,LevoParadoxPPOHybrid,-0.3010780110926538,neutral,0.4882761538028717
+954,3,154,1745086003,LevoParadoxPPOHybrid,1.3934767393490588,neutral,0.49011749029159546
+955,3,155,1329952935,LevoParadoxPPOHybrid,0.4028712963253857,neutral,0.48847508430480957
+956,3,156,129216577,LevoParadoxPPOHybrid,1.6226525683891055,neutral,0.48973292112350464
+957,3,157,264447624,LevoParadoxPPOHybrid,-0.2925163838635117,neutral,0.48956099152565
+958,3,158,1239906804,LevoParadoxPPOHybrid,-0.3826083405585227,neutral,0.4878310561180115
+959,3,159,1968251934,LevoParadoxPPOHybrid,-3.0,overconfident,0.4882761538028717
+960,3,160,1710361518,LevoParadoxPPOHybrid,0.24410679553415088,neutral,0.4859134256839752
+961,3,161,1919087800,LevoParadoxPPOHybrid,1.3080289495019592,neutral,0.48261088132858276
+962,3,162,1038210496,LevoParadoxPPOHybrid,-0.795060643378411,neutral,0.4878310561180115
+963,3,163,1786593305,LevoParadoxPPOHybrid,-0.4133444185482581,neutral,0.48956099152565
+964,3,164,2098346951,LevoParadoxPPOHybrid,0.37264748440059975,neutral,0.4876002371311188
+965,3,165,1526904777,LevoParadoxPPOHybrid,0.24330040162795474,neutral,0.4853600561618805
+966,3,166,1540522504,LevoParadoxPPOHybrid,0.3133023273996773,neutral,0.4841948449611664
+967,3,167,2109348201,LevoParadoxPPOHybrid,0.6798652475070099,neutral,0.4832690358161926
+968,3,168,1132622124,LevoParadoxPPOHybrid,-0.6901294612165475,overcautious,0.4896336793899536
+969,3,169,1615465936,LevoParadoxPPOHybrid,-2.3064563431034584,overconfident,0.49011749029159546
+970,3,170,364326948,LevoParadoxPPOHybrid,1.0815486952393436,neutral,0.4876002371311188
+971,3,171,1220814450,LevoParadoxPPOHybrid,0.8723429375012457,neutral,0.4841948449611664
+972,3,172,731873456,LevoParadoxPPOHybrid,0.612363357663838,neutral,0.4859134256839752
+973,3,173,1247536227,LevoParadoxPPOHybrid,-0.8071191221540742,neutral,0.48261088132858276
+974,3,174,1337517349,LevoParadoxPPOHybrid,-0.04748141792384848,neutral,0.48956099152565
+975,3,175,988735470,LevoParadoxPPOHybrid,-0.22941818391398483,neutral,0.49009424448013306
+976,3,176,624782456,LevoParadoxPPOHybrid,0.20896413148081383,neutral,0.49009424448013306
+977,3,177,1614653143,LevoParadoxPPOHybrid,-0.4293029498830948,neutral,0.4856588840484619
+978,3,178,1500038813,LevoParadoxPPOHybrid,1.1161432423615547,neutral,0.4856588840484619
+979,3,179,2130149343,LevoParadoxPPOHybrid,-0.6482581247171009,neutral,0.4832690358161926
+980,3,180,132980447,LevoParadoxPPOHybrid,-1.6670530094964546,neutral,0.4896336793899536
+981,3,181,1952726781,LevoParadoxPPOHybrid,-2.434336344440533,overconfident,0.48763468861579895
+982,3,182,555625828,LevoParadoxPPOHybrid,0.7003550926274642,neutral,0.4876002371311188
+983,3,183,498814370,LevoParadoxPPOHybrid,-0.2862536747479175,neutral,0.48261088132858276
+984,3,184,206366738,LevoParadoxPPOHybrid,0.8775826217617475,neutral,0.48956099152565
+985,3,185,400273478,LevoParadoxPPOHybrid,-0.2835608641021956,neutral,0.4832690358161926
+986,3,186,670771760,LevoParadoxPPOHybrid,0.14429033269177619,neutral,0.4859134256839752
+987,3,187,1820812129,LevoParadoxPPOHybrid,1.1935862197370715,neutral,0.48956099152565
+988,3,188,1243239295,LevoParadoxPPOHybrid,-1.127181247498464,overcautious,0.4853600561618805
+989,3,189,1980464422,LevoParadoxPPOHybrid,0.2808684424586057,neutral,0.4856588840484619
+990,3,190,1156736002,LevoParadoxPPOHybrid,-0.28270551923738596,neutral,0.48956099152565
+991,3,191,1697044130,LevoParadoxPPOHybrid,0.4826422855105287,neutral,0.48912549018859863
+992,3,192,562498219,LevoParadoxPPOHybrid,0.07891803981678115,neutral,0.4859134256839752
+993,3,193,1878178784,LevoParadoxPPOHybrid,0.20228392169117065,neutral,0.48956099152565
+994,3,194,2065152970,LevoParadoxPPOHybrid,1.3065279618298171,neutral,0.48261088132858276
+995,3,195,775550551,LevoParadoxPPOHybrid,0.7688120776761941,neutral,0.4876002371311188
+996,3,196,1056375082,LevoParadoxPPOHybrid,2.643885410414239,neutral,0.48912549018859863
+997,3,197,1600998019,LevoParadoxPPOHybrid,0.04270551606696389,neutral,0.4853600561618805
+998,3,198,1027113059,LevoParadoxPPOHybrid,0.6819952539956415,neutral,0.48763468861579895
+999,3,199,609138227,LevoParadoxPPOHybrid,1.2826738383017549,neutral,0.4876002371311188
+1000,3,200,122699067,LevoParadoxPPOHybrid,1.4645920019362932,neutral,0.4882761538028717
+1001,3,201,365381056,LevoParadoxPPOHybrid,0.1175398932744254,neutral,0.49011749029159546
+1002,3,202,1238636582,LevoParadoxPPOHybrid,1.5278968205399832,neutral,0.4853600561618805
+1003,3,203,1658289347,LevoParadoxPPOHybrid,-2.3608989109395417,overconfident,0.4882761538028717
+1004,3,204,1322727040,LevoParadoxPPOHybrid,-0.5812129563754665,neutral,0.49009424448013306
+1005,3,205,2126230739,LevoParadoxPPOHybrid,0.055286863165256224,neutral,0.4856588840484619
+1006,3,206,289701504,LevoParadoxPPOHybrid,-0.6531967872653202,neutral,0.4832690358161926
+1007,3,207,1261784629,LevoParadoxPPOHybrid,0.1842492194028491,neutral,0.48342037200927734
+1008,3,208,1377412115,LevoParadoxPPOHybrid,0.6245206594688067,neutral,0.48763468861579895
+1009,3,209,1724786786,LevoParadoxPPOHybrid,0.8985310433229601,neutral,0.48956099152565
+1010,3,210,89300232,LevoParadoxPPOHybrid,0.6123175354440541,neutral,0.48261088132858276
+1011,3,211,1017426355,LevoParadoxPPOHybrid,1.4029986467083133,neutral,0.48261088132858276
+1012,3,212,1395142654,LevoParadoxPPOHybrid,1.3212883154759467,neutral,0.48763468861579895
+1013,3,213,939770990,LevoParadoxPPOHybrid,0.3690192392322482,neutral,0.48847508430480957
+1014,3,214,1446797165,LevoParadoxPPOHybrid,2.042922515816966,neutral,0.48912549018859863
+1015,3,215,464152649,LevoParadoxPPOHybrid,0.66230012650082,neutral,0.48956099152565
+1016,3,216,495168830,LevoParadoxPPOHybrid,2.231253447189229,neutral,0.4853600561618805
+1017,3,217,1239321323,LevoParadoxPPOHybrid,0.7971781308027754,neutral,0.4832690358161926
+1018,3,218,1388207032,LevoParadoxPPOHybrid,0.19859386994917153,neutral,0.4841948449611664
+1019,3,219,1731639298,LevoParadoxPPOHybrid,-0.01593363519287893,neutral,0.48261088132858276
+1020,3,220,1605648698,LevoParadoxPPOHybrid,0.5776867006145332,neutral,0.4841948449611664
+1021,3,221,281650086,LevoParadoxPPOHybrid,0.1894277803351234,neutral,0.4832690358161926
+1022,3,222,931350263,LevoParadoxPPOHybrid,0.98787063742711,neutral,0.4841948449611664
+1023,3,223,691328288,LevoParadoxPPOHybrid,0.0743356805063284,neutral,0.48261088132858276
+1024,3,224,633318446,LevoParadoxPPOHybrid,1.8361888649880167,neutral,0.48833152651786804
+1025,3,225,1081427988,LevoParadoxPPOHybrid,0.1338333666167951,neutral,0.49327588081359863
+1026,3,226,115545717,LevoParadoxPPOHybrid,1.360667887399388,neutral,0.48658421635627747
+1027,3,227,309528326,LevoParadoxPPOHybrid,-1.4173429922782725,overcautious,0.49301716685295105
+1028,3,228,396994132,LevoParadoxPPOHybrid,0.22439107870340078,neutral,0.49331241846084595
+1029,3,229,1235689954,LevoParadoxPPOHybrid,0.266905886575473,neutral,0.489418625831604
+1030,3,230,992703584,LevoParadoxPPOHybrid,0.3321246635033697,neutral,0.49285888671875
+1031,3,231,1080333835,LevoParadoxPPOHybrid,1.6931666633730826,neutral,0.4898187220096588
+1032,3,232,610075266,LevoParadoxPPOHybrid,0.1761494721175345,neutral,0.49076247215270996
+1033,3,233,131753921,LevoParadoxPPOHybrid,0.543341071924465,neutral,0.4936416745185852
+1034,3,234,1547145187,LevoParadoxPPOHybrid,-0.09291703783075383,neutral,0.48658421635627747
+1035,3,235,1112526292,LevoParadoxPPOHybrid,0.9173411436063477,neutral,0.4916113317012787
+1036,3,236,1114040609,LevoParadoxPPOHybrid,-0.466186548405527,neutral,0.48658421635627747
+1037,3,237,9016300,LevoParadoxPPOHybrid,0.5729399550735099,neutral,0.4916086792945862
+1038,3,238,2021796480,LevoParadoxPPOHybrid,1.1871455134687214,neutral,0.48658421635627747
+1039,3,239,1032777022,LevoParadoxPPOHybrid,1.5605861822412053,neutral,0.49060913920402527
+1040,3,240,430409080,LevoParadoxPPOHybrid,0.47291951840375646,neutral,0.486750990152359
+1041,3,241,1399360085,LevoParadoxPPOHybrid,-2.6455916936013986,overconfident,0.4898187220096588
+1042,3,242,436964639,LevoParadoxPPOHybrid,2.487891437358731,neutral,0.49301716685295105
+1043,3,243,1154674875,LevoParadoxPPOHybrid,1.0635383620472745,neutral,0.48833152651786804
+1044,3,244,1018060628,LevoParadoxPPOHybrid,0.9310030302878805,neutral,0.4895656704902649
+1045,3,245,302524943,LevoParadoxPPOHybrid,0.050867633534433285,neutral,0.489418625831604
+1046,3,246,993587315,LevoParadoxPPOHybrid,0.2719540899073052,neutral,0.489418625831604
+1047,3,247,821789357,LevoParadoxPPOHybrid,1.6952904194648668,neutral,0.49283483624458313
+1048,3,248,2140712286,LevoParadoxPPOHybrid,0.17938752569393754,neutral,0.4895656704902649
+1049,3,249,791580549,LevoParadoxPPOHybrid,0.5520582714410324,neutral,0.49327588081359863
+1050,3,250,1895531684,LevoParadoxPPOHybrid,-0.2268423785723466,neutral,0.48658421635627747
+1051,3,251,245446075,LevoParadoxPPOHybrid,1.4389144622355732,neutral,0.49060913920402527
+1052,3,252,528694894,LevoParadoxPPOHybrid,0.2077126290733675,neutral,0.489418625831604
+1053,3,253,383543867,LevoParadoxPPOHybrid,-0.5433073101045013,neutral,0.4916113317012787
+1054,3,254,507831984,LevoParadoxPPOHybrid,0.7572496251706334,neutral,0.4916113317012787
+1055,3,255,1866575340,LevoParadoxPPOHybrid,0.6129199969750596,neutral,0.49285888671875
+1056,3,256,1604134655,LevoParadoxPPOHybrid,1.0593717973391175,neutral,0.4866838753223419
+1057,3,257,2099757217,LevoParadoxPPOHybrid,-2.40684095296509,overconfident,0.49060913920402527
+1058,3,258,113075702,LevoParadoxPPOHybrid,1.5841466214856086,neutral,0.49283483624458313
+1059,3,259,1114107496,LevoParadoxPPOHybrid,-0.40426330871151356,neutral,0.49060913920402527
+1060,3,260,540143375,LevoParadoxPPOHybrid,-0.3669807746773094,neutral,0.4866838753223419
+1061,3,261,1516404,LevoParadoxPPOHybrid,2.304766172762145,neutral,0.49301716685295105
+1062,3,262,444535043,LevoParadoxPPOHybrid,1.689358900483771,neutral,0.49060913920402527
+1063,3,263,413354163,LevoParadoxPPOHybrid,0.4654184462253926,neutral,0.4916113317012787
+1064,3,264,554663744,LevoParadoxPPOHybrid,1.1133992594778106,neutral,0.4936416745185852
+1065,3,265,903269569,LevoParadoxPPOHybrid,0.8714377976361631,neutral,0.4916086792945862
+1066,3,266,1932585699,LevoParadoxPPOHybrid,0.23303728348539654,neutral,0.49327588081359863
+1067,3,267,127998833,LevoParadoxPPOHybrid,-0.3177470682658397,neutral,0.49285888671875
+1068,3,268,880556158,LevoParadoxPPOHybrid,1.106183553403016,neutral,0.4916113317012787
+1069,3,269,936001589,LevoParadoxPPOHybrid,0.7549588091802616,neutral,0.4936416745185852
+1070,3,270,1501301443,LevoParadoxPPOHybrid,0.9736562726861935,neutral,0.4936416745185852
+1071,3,271,19383980,LevoParadoxPPOHybrid,0.6944357474098843,neutral,0.49331241846084595
+1072,3,272,1157426772,LevoParadoxPPOHybrid,-1.7601137690719457,overconfident,0.49060913920402527
+1073,3,273,1952228844,LevoParadoxPPOHybrid,-0.7176220302503407,neutral,0.486750990152359
+1074,3,274,1211030371,LevoParadoxPPOHybrid,1.7340914342199532,neutral,0.48833152651786804
+1075,3,275,1321059392,LevoParadoxPPOHybrid,-0.8466103390976051,neutral,0.4936416745185852
+1076,3,276,1798791588,LevoParadoxPPOHybrid,-1.9097750416271517,overconfident,0.49060913920402527
+1077,3,277,1246246285,LevoParadoxPPOHybrid,0.8795901381968463,neutral,0.49285888671875
+1078,3,278,457172572,LevoParadoxPPOHybrid,0.4305942352110612,neutral,0.4936416745185852
+1079,3,279,448736337,LevoParadoxPPOHybrid,0.6904533826305582,neutral,0.4916113317012787
+1080,3,280,1299406706,LevoParadoxPPOHybrid,1.4523638955546416,neutral,0.4895656704902649
+1081,3,281,1264773565,LevoParadoxPPOHybrid,0.6735045597096321,neutral,0.49331241846084595
+1082,3,282,1354099525,LevoParadoxPPOHybrid,-0.9555847332433495,neutral,0.49285888671875
+1083,3,283,372058917,LevoParadoxPPOHybrid,0.7365455977878236,neutral,0.489418625831604
+1084,3,284,1037785478,LevoParadoxPPOHybrid,-0.4828807234404753,neutral,0.48658421635627747
+1085,3,285,1811624747,LevoParadoxPPOHybrid,0.6071438024245759,neutral,0.4858545958995819
+1086,3,286,1038158713,LevoParadoxPPOHybrid,0.6997274493131587,neutral,0.489418625831604
+1087,3,287,2093019788,LevoParadoxPPOHybrid,-1.0100227353420415,neutral,0.489418625831604
+1088,3,288,212225853,LevoParadoxPPOHybrid,-0.38702110349628943,neutral,0.486750990152359
+1089,3,289,780127530,LevoParadoxPPOHybrid,1.3053559696215205,neutral,0.4916113317012787
+1090,3,290,708820677,LevoParadoxPPOHybrid,0.6396186228494765,neutral,0.486750990152359
+1091,3,291,1943826058,LevoParadoxPPOHybrid,0.5280049650751485,neutral,0.4858545958995819
+1092,3,292,1228600845,LevoParadoxPPOHybrid,2.351804957931147,neutral,0.49283483624458313
+1093,3,293,1907054980,LevoParadoxPPOHybrid,0.47626137268114643,neutral,0.49285888671875
+1094,3,294,684222005,LevoParadoxPPOHybrid,2.0141844440006436,neutral,0.4898187220096588
+1095,3,295,1264548661,LevoParadoxPPOHybrid,0.9825214353567251,neutral,0.4898187220096588
+1096,3,296,1936568871,LevoParadoxPPOHybrid,-1.105227888104164,overcautious,0.48833152651786804
+1097,3,297,1497319181,LevoParadoxPPOHybrid,0.06488571313026902,neutral,0.4916086792945862
+1098,3,298,97345709,LevoParadoxPPOHybrid,-0.269915704835884,neutral,0.4916086792945862
+1099,3,299,652059175,LevoParadoxPPOHybrid,1.4430543711983108,neutral,0.49331241846084595
+1100,3,300,130313659,LevoParadoxPPOHybrid,1.4325859998188804,neutral,0.4936416745185852
+1101,3,301,1373428940,LevoParadoxPPOHybrid,0.22583825072159266,neutral,0.4866838753223419
+1102,3,302,1763220031,LevoParadoxPPOHybrid,0.5867242898117275,neutral,0.4858545958995819
+1103,3,303,766053327,LevoParadoxPPOHybrid,2.286235206364041,neutral,0.49283483624458313
+1104,3,304,186108448,LevoParadoxPPOHybrid,1.687379567654013,neutral,0.48833152651786804
+1105,3,305,1525727770,LevoParadoxPPOHybrid,-0.2806885114658522,neutral,0.49283483624458313
+1106,3,306,1723298298,LevoParadoxPPOHybrid,-2.053392775572538,overconfident,0.4898187220096588
+1107,3,307,1809177412,LevoParadoxPPOHybrid,-0.21809959973084328,neutral,0.4916086792945862
+1108,3,308,808471678,LevoParadoxPPOHybrid,-0.21586492152146408,neutral,0.48658421635627747
+1109,3,309,1012658889,LevoParadoxPPOHybrid,0.659992373614612,neutral,0.489418625831604
+1110,3,310,123240018,LevoParadoxPPOHybrid,-0.02788444401156496,neutral,0.49076247215270996
+1111,3,311,595923089,LevoParadoxPPOHybrid,-0.45128377826996513,overcautious,0.49283483624458313
+1112,3,312,64946568,LevoParadoxPPOHybrid,1.0220192129178896,neutral,0.4866838753223419
+1113,3,313,1694054676,LevoParadoxPPOHybrid,3.0,neutral,0.49283483624458313
+1114,3,314,1378532479,LevoParadoxPPOHybrid,1.6602005302173342,neutral,0.48833152651786804
+1115,3,315,294177744,LevoParadoxPPOHybrid,0.9431097985305882,neutral,0.4866838753223419
+1116,3,316,382616457,LevoParadoxPPOHybrid,0.19900701204930193,neutral,0.489418625831604
+1117,3,317,654022692,LevoParadoxPPOHybrid,0.5937927035952054,neutral,0.48658421635627747
+1118,3,318,1938298404,LevoParadoxPPOHybrid,-1.83814468538334,overconfident,0.4898187220096588
+1119,3,319,1251753220,LevoParadoxPPOHybrid,0.25922762062913374,neutral,0.49076247215270996
+1120,3,320,164235737,LevoParadoxPPOHybrid,0.9360725189336654,neutral,0.4916113317012787
+1121,3,321,1176577515,LevoParadoxPPOHybrid,-0.07777068337054072,neutral,0.4866838753223419
+1122,3,322,66131236,LevoParadoxPPOHybrid,0.08199780321756987,neutral,0.4916113317012787
+1123,3,323,816614407,LevoParadoxPPOHybrid,1.8730655609120999,neutral,0.48833152651786804
+1124,3,324,1678449735,LevoParadoxPPOHybrid,0.5252050183718692,neutral,0.486750990152359
+1125,3,325,155847418,LevoParadoxPPOHybrid,-0.02681169897223142,neutral,0.4936416745185852
+1126,3,326,385469728,LevoParadoxPPOHybrid,2.376726011756658,neutral,0.49283483624458313
+1127,3,327,2122723976,LevoParadoxPPOHybrid,1.4471731631475728,neutral,0.4866838753223419
+1128,3,328,1933100203,LevoParadoxPPOHybrid,-2.0497120652466028,overconfident,0.4898187220096588
+1129,3,329,650925416,LevoParadoxPPOHybrid,-0.49579878845179687,neutral,0.489418625831604
+1130,3,330,1094127659,LevoParadoxPPOHybrid,-0.07141335729505123,neutral,0.48658421635627747
+1131,3,331,876865270,LevoParadoxPPOHybrid,-0.5407918798944782,neutral,0.49327588081359863
+1132,3,332,1319422772,LevoParadoxPPOHybrid,1.6276730098197536,neutral,0.49301716685295105
+1133,3,333,174465858,LevoParadoxPPOHybrid,0.2544404882028042,neutral,0.49076247215270996
+1134,3,334,2060489424,LevoParadoxPPOHybrid,0.2477776674203278,neutral,0.4898187220096588
+1135,3,335,1621988536,LevoParadoxPPOHybrid,-0.48591510057403453,neutral,0.49331241846084595
+1136,3,336,965810292,LevoParadoxPPOHybrid,0.049978842421051245,neutral,0.49301716685295105
+1137,3,337,2093565938,LevoParadoxPPOHybrid,2.4772099381580697,neutral,0.49301716685295105
+1138,3,338,1650235702,LevoParadoxPPOHybrid,0.1418919856891526,neutral,0.4858545958995819
+1139,3,339,511412748,LevoParadoxPPOHybrid,0.6346451568587965,neutral,0.489418625831604
+1140,3,340,922611389,LevoParadoxPPOHybrid,-0.8616451980838941,overcautious,0.48833152651786804
+1141,3,341,652729866,LevoParadoxPPOHybrid,1.948976720949957,neutral,0.49331241846084595
+1142,3,342,221600474,LevoParadoxPPOHybrid,0.9973383239464882,neutral,0.4916113317012787
+1143,3,343,31421970,LevoParadoxPPOHybrid,0.02929702365773283,neutral,0.4866838753223419
+1144,3,344,1192414163,LevoParadoxPPOHybrid,0.6691241621227473,neutral,0.4916086792945862
+1145,3,345,649875877,LevoParadoxPPOHybrid,0.7070359587513864,neutral,0.4895656704902649
+1146,3,346,105131269,LevoParadoxPPOHybrid,2.652461124351755,neutral,0.48833152651786804
+1147,3,347,156751511,LevoParadoxPPOHybrid,0.6272225859885717,neutral,0.4895656704902649
+1148,3,348,761801730,LevoParadoxPPOHybrid,-0.5934301134706246,neutral,0.4895656704902649
+1149,3,349,2081080430,LevoParadoxPPOHybrid,0.5738879582462928,neutral,0.4866838753223419
+1150,3,350,2015553861,LevoParadoxPPOHybrid,-1.4177993539631866,neutral,0.4895656704902649
+1151,3,351,1908286303,LevoParadoxPPOHybrid,1.506092772192412,neutral,0.4895656704902649
+1152,3,352,911061556,LevoParadoxPPOHybrid,1.3687399763929848,neutral,0.49285888671875
+1153,3,353,34499089,LevoParadoxPPOHybrid,-0.9282651241178062,overcautious,0.49301716685295105
+1154,3,354,2098425720,LevoParadoxPPOHybrid,-0.9085834687581021,neutral,0.4936416745185852
+1155,3,355,1106840722,LevoParadoxPPOHybrid,-0.24296696671554552,overcautious,0.49301716685295105
+1156,3,356,317986024,LevoParadoxPPOHybrid,0.5275187514191623,neutral,0.4916086792945862
+1157,3,357,129256349,LevoParadoxPPOHybrid,1.1843746050806352,neutral,0.49327588081359863
+1158,3,358,431303789,LevoParadoxPPOHybrid,0.071342761929298,overcautious,0.49301716685295105
+1159,3,359,523632446,LevoParadoxPPOHybrid,0.08805085928707428,neutral,0.4858545958995819
+1160,3,360,1950687447,LevoParadoxPPOHybrid,0.26743054682592193,neutral,0.49076247215270996
+1161,3,361,1411833232,LevoParadoxPPOHybrid,0.8063372079804664,neutral,0.4895656704902649
+1162,3,362,690319881,LevoParadoxPPOHybrid,-1.4350170010233716,overconfident,0.49327588081359863
+1163,3,363,175246150,LevoParadoxPPOHybrid,0.8602184244464084,neutral,0.49060913920402527
+1164,3,364,1182674932,LevoParadoxPPOHybrid,1.7870325658814412,neutral,0.4898187220096588
+1165,3,365,393595655,LevoParadoxPPOHybrid,0.049699194185405235,neutral,0.48658421635627747
+1166,3,366,1029071917,LevoParadoxPPOHybrid,-0.5707083935878005,neutral,0.48658421635627747
+1167,3,367,440607926,LevoParadoxPPOHybrid,0.3759669685649799,neutral,0.48658421635627747
+1168,3,368,990295427,LevoParadoxPPOHybrid,0.7107320242384884,neutral,0.4858545958995819
+1169,3,369,1533915152,LevoParadoxPPOHybrid,0.1294248189887542,neutral,0.486750990152359
+1170,3,370,2028880655,LevoParadoxPPOHybrid,1.0673159176286864,neutral,0.4866838753223419
+1171,3,371,1110082952,LevoParadoxPPOHybrid,-0.14934540849956535,neutral,0.489418625831604
+1172,3,372,1351612215,LevoParadoxPPOHybrid,0.4566579866558717,neutral,0.489418625831604
+1173,3,373,161873384,LevoParadoxPPOHybrid,0.4425368930740302,neutral,0.49331241846084595
+1174,3,374,1943568012,LevoParadoxPPOHybrid,-1.0470027903099404,overcautious,0.48833152651786804
+1175,3,375,27538724,LevoParadoxPPOHybrid,1.1698618251818689,neutral,0.49327588081359863
+1176,3,376,534070892,LevoParadoxPPOHybrid,0.204998638571319,neutral,0.48658421635627747
+1177,3,377,1484958114,LevoParadoxPPOHybrid,1.2965587422443934,neutral,0.49285888671875
+1178,3,378,1217763652,LevoParadoxPPOHybrid,1.4505600122642395,neutral,0.4898187220096588
+1179,3,379,951904837,LevoParadoxPPOHybrid,-0.6140963258839023,neutral,0.4916113317012787
+1180,3,380,1611118613,LevoParadoxPPOHybrid,0.7767451020137636,neutral,0.4895656704902649
+1181,3,381,518917746,LevoParadoxPPOHybrid,-0.3309841660977714,neutral,0.49285888671875
+1182,3,382,64896530,LevoParadoxPPOHybrid,-1.7495881572101664,overconfident,0.49060913920402527
+1183,3,383,703323482,LevoParadoxPPOHybrid,0.39758457782113854,neutral,0.49285888671875
+1184,3,384,878877531,LevoParadoxPPOHybrid,-0.45663118508894723,neutral,0.49076247215270996
+1185,3,385,743424612,LevoParadoxPPOHybrid,0.15881701687226527,neutral,0.4898187220096588
+1186,3,386,1611600344,LevoParadoxPPOHybrid,1.1958528378072517,neutral,0.486750990152359
+1187,3,387,1653110007,LevoParadoxPPOHybrid,-1.294775431953231,overcautious,0.48833152651786804
+1188,3,388,1477247030,LevoParadoxPPOHybrid,0.06359680112012978,neutral,0.49060913920402527
+1189,3,389,460418126,LevoParadoxPPOHybrid,2.5444399454267375,neutral,0.49283483624458313
+1190,3,390,965728184,LevoParadoxPPOHybrid,-0.5056482391152837,neutral,0.4936416745185852
+1191,3,391,800112339,LevoParadoxPPOHybrid,1.3941253259697812,neutral,0.4866838753223419
+1192,3,392,1881978705,LevoParadoxPPOHybrid,0.11355250158904308,neutral,0.4858545958995819
+1193,3,393,765013452,LevoParadoxPPOHybrid,0.1964416250166414,neutral,0.49331241846084595
+1194,3,394,1327556692,LevoParadoxPPOHybrid,0.27807276108114626,neutral,0.49076247215270996
+1195,3,395,149816571,LevoParadoxPPOHybrid,0.536807514032734,neutral,0.49331241846084595
+1196,3,396,338046121,LevoParadoxPPOHybrid,0.3025212168232634,neutral,0.48833152651786804
+1197,3,397,994124776,LevoParadoxPPOHybrid,0.38399826581662966,neutral,0.49076247215270996
+1198,3,398,713057376,LevoParadoxPPOHybrid,1.3402040989179698,neutral,0.4866838753223419
+1199,3,399,1118891224,LevoParadoxPPOHybrid,-0.48269914185478485,neutral,0.489418625831604
diff --git a/Down/down/results/paradox_tabular_results.csv b/Down/down/results/paradox_tabular_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5efdeb1d1448e91c4e9f1308d49e86055be464f8
--- /dev/null
+++ b/Down/down/results/paradox_tabular_results.csv
@@ -0,0 +1,2401 @@
+global_episode,phase,episode,env_seed,agent,episode_reward,failure_mode,rho_state
+0,1,0,960941037,HFLevo,1.3,neutral,nan
+0,1,0,960941037,LevoParadoxIsomer,0.0,neutral,0.5
+1,1,1,2135581874,HFLevo,1.3,neutral,nan
+1,1,1,2135581874,LevoParadoxIsomer,0.0,neutral,0.5
+2,1,2,2131554306,HFLevo,1.3,neutral,nan
+2,1,2,2131554306,LevoParadoxIsomer,0.0,neutral,0.5
+3,1,3,820359671,HFLevo,-2.0,overconfident,nan
+3,1,3,820359671,LevoParadoxIsomer,-2.0,overconfident,0.5
+4,1,4,2047814545,HFLevo,1.3,neutral,nan
+4,1,4,2047814545,LevoParadoxIsomer,1.3,neutral,0.5
+5,1,5,1776286830,HFLevo,1.2,neutral,nan
+5,1,5,1776286830,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+6,1,6,1373214917,HFLevo,-2.0,overconfident,nan
+6,1,6,1373214917,LevoParadoxIsomer,-2.0,overconfident,0.5
+7,1,7,1797992013,HFLevo,1.3,neutral,nan
+7,1,7,1797992013,LevoParadoxIsomer,0.0,neutral,0.5
+8,1,8,1645535739,HFLevo,1.2,neutral,nan
+8,1,8,1645535739,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+9,1,9,2095533891,HFLevo,1.3,neutral,nan
+9,1,9,2095533891,LevoParadoxIsomer,1.3,neutral,0.5
+10,1,10,829401385,HFLevo,1.3,neutral,nan
+10,1,10,829401385,LevoParadoxIsomer,1.3,neutral,0.5
+11,1,11,165839486,HFLevo,1.3,neutral,nan
+11,1,11,165839486,LevoParadoxIsomer,1.3,neutral,0.5
+12,1,12,976680886,HFLevo,1.2,neutral,nan
+12,1,12,976680886,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+13,1,13,681732448,HFLevo,0.0,neutral,nan
+13,1,13,681732448,LevoParadoxIsomer,0.0,neutral,0.5
+14,1,14,1046126860,HFLevo,1.3,neutral,nan
+14,1,14,1046126860,LevoParadoxIsomer,0.0,neutral,0.5
+15,1,15,1974730198,HFLevo,0.0,neutral,nan
+15,1,15,1974730198,LevoParadoxIsomer,1.3,neutral,0.5
+16,1,16,2073558604,HFLevo,0.0,neutral,nan
+16,1,16,2073558604,LevoParadoxIsomer,0.0,neutral,0.5
+17,1,17,1451418837,HFLevo,0.0,neutral,nan
+17,1,17,1451418837,LevoParadoxIsomer,1.3,neutral,0.5
+18,1,18,1365011226,HFLevo,1.3,neutral,nan
+18,1,18,1365011226,LevoParadoxIsomer,1.3,neutral,0.5
+19,1,19,613804374,HFLevo,1.3,neutral,nan
+19,1,19,613804374,LevoParadoxIsomer,1.3,neutral,0.5
+20,1,20,32280267,HFLevo,1.3,neutral,nan
+20,1,20,32280267,LevoParadoxIsomer,0.0,neutral,0.5
+21,1,21,836537275,HFLevo,0.0,neutral,nan
+21,1,21,836537275,LevoParadoxIsomer,0.0,neutral,0.5
+22,1,22,975647501,HFLevo,0.0,neutral,nan
+22,1,22,975647501,LevoParadoxIsomer,-0.8,overcautious,0.5
+23,1,23,494392738,HFLevo,1.3,neutral,nan
+23,1,23,494392738,LevoParadoxIsomer,0.0,neutral,0.5
+24,1,24,1706901824,HFLevo,0.0,neutral,nan
+24,1,24,1706901824,LevoParadoxIsomer,0.0,neutral,0.5
+25,1,25,358546340,HFLevo,0.0,neutral,nan
+25,1,25,358546340,LevoParadoxIsomer,1.3,neutral,0.5
+26,1,26,1030268022,HFLevo,-2.0,overconfident,nan
+26,1,26,1030268022,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+27,1,27,329502221,HFLevo,0.0,neutral,nan
+27,1,27,329502221,LevoParadoxIsomer,0.0,neutral,0.5
+28,1,28,746578914,HFLevo,1.3,neutral,nan
+28,1,28,746578914,LevoParadoxIsomer,0.0,neutral,0.5
+29,1,29,2091845998,HFLevo,1.3,neutral,nan
+29,1,29,2091845998,LevoParadoxIsomer,1.3,neutral,0.5
+30,1,30,1436029081,HFLevo,0.0,neutral,nan
+30,1,30,1436029081,LevoParadoxIsomer,1.3,neutral,0.5
+31,1,31,910309404,HFLevo,0.0,neutral,nan
+31,1,31,910309404,LevoParadoxIsomer,1.3,neutral,0.5
+32,1,32,472048561,HFLevo,1.3,neutral,nan
+32,1,32,472048561,LevoParadoxIsomer,0.0,neutral,0.5
+33,1,33,135600480,HFLevo,0.0,neutral,nan
+33,1,33,135600480,LevoParadoxIsomer,0.0,neutral,0.5
+34,1,34,1892894588,HFLevo,0.0,neutral,nan
+34,1,34,1892894588,LevoParadoxIsomer,1.3,neutral,0.5
+35,1,35,1055673936,HFLevo,1.2,neutral,nan
+35,1,35,1055673936,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+36,1,36,1340667110,HFLevo,0.0,neutral,nan
+36,1,36,1340667110,LevoParadoxIsomer,0.0,neutral,0.5
+37,1,37,619212679,HFLevo,-2.0,overconfident,nan
+37,1,37,619212679,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+38,1,38,120545107,HFLevo,1.3,neutral,nan
+38,1,38,120545107,LevoParadoxIsomer,1.3,neutral,0.5
+39,1,39,454442609,HFLevo,2.5,neutral,nan
+39,1,39,454442609,LevoParadoxIsomer,2.5,neutral,0.5
+40,1,40,295287540,HFLevo,0.0,neutral,nan
+40,1,40,295287540,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+41,1,41,445457645,HFLevo,1.3,neutral,nan
+41,1,41,445457645,LevoParadoxIsomer,1.3,neutral,0.5
+42,1,42,307622279,HFLevo,1.3,neutral,nan
+42,1,42,307622279,LevoParadoxIsomer,1.3,neutral,0.5
+43,1,43,1243437117,HFLevo,0.0,neutral,nan
+43,1,43,1243437117,LevoParadoxIsomer,0.0,neutral,0.5
+44,1,44,244133596,HFLevo,0.0,neutral,nan
+44,1,44,244133596,LevoParadoxIsomer,1.3,neutral,0.5
+45,1,45,1268841274,HFLevo,0.0,neutral,nan
+45,1,45,1268841274,LevoParadoxIsomer,0.0,neutral,0.5
+46,1,46,1225761364,HFLevo,1.3,neutral,nan
+46,1,46,1225761364,LevoParadoxIsomer,0.0,neutral,0.5
+47,1,47,1907103971,HFLevo,1.3,neutral,nan
+47,1,47,1907103971,LevoParadoxIsomer,1.3,neutral,0.5
+48,1,48,1034799201,HFLevo,0.0,neutral,nan
+48,1,48,1034799201,LevoParadoxIsomer,0.0,neutral,0.5
+49,1,49,235864475,HFLevo,-2.0,overconfident,nan
+49,1,49,235864475,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+50,1,50,691379092,HFLevo,-2.0,overconfident,nan
+50,1,50,691379092,LevoParadoxIsomer,1.2,neutral,0.5
+51,1,51,1065800727,HFLevo,2.5,neutral,nan
+51,1,51,1065800727,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+52,1,52,655184241,HFLevo,-2.0,overconfident,nan
+52,1,52,655184241,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+53,1,53,623054291,HFLevo,2.5,neutral,nan
+53,1,53,623054291,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+54,1,54,540036626,HFLevo,-0.8,overcautious,nan
+54,1,54,540036626,LevoParadoxIsomer,0.0,neutral,0.5
+55,1,55,1043358048,HFLevo,1.2,neutral,nan
+55,1,55,1043358048,LevoParadoxIsomer,1.2,neutral,0.5
+56,1,56,1932965406,HFLevo,1.3,neutral,nan
+56,1,56,1932965406,LevoParadoxIsomer,0.0,neutral,0.5
+57,1,57,2036930070,HFLevo,1.3,neutral,nan
+57,1,57,2036930070,LevoParadoxIsomer,1.3,neutral,0.5
+58,1,58,1526743940,HFLevo,1.3,neutral,nan
+58,1,58,1526743940,LevoParadoxIsomer,0.0,neutral,0.5
+59,1,59,1989662969,HFLevo,0.0,neutral,nan
+59,1,59,1989662969,LevoParadoxIsomer,-0.8,overcautious,0.5
+60,1,60,2139514329,HFLevo,0.0,neutral,nan
+60,1,60,2139514329,LevoParadoxIsomer,0.0,neutral,0.5
+61,1,61,1632303898,HFLevo,0.0,neutral,nan
+61,1,61,1632303898,LevoParadoxIsomer,1.3,neutral,0.5
+62,1,62,1850997508,HFLevo,1.3,neutral,nan
+62,1,62,1850997508,LevoParadoxIsomer,0.0,neutral,0.5
+63,1,63,1679388031,HFLevo,1.3,neutral,nan
+63,1,63,1679388031,LevoParadoxIsomer,0.0,neutral,0.5
+64,1,64,994656789,HFLevo,0.0,neutral,nan
+64,1,64,994656789,LevoParadoxIsomer,1.3,neutral,0.5
+65,1,65,596802772,HFLevo,0.0,neutral,nan
+65,1,65,596802772,LevoParadoxIsomer,1.3,neutral,0.5
+66,1,66,2022213408,HFLevo,-0.8,overcautious,nan
+66,1,66,2022213408,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+67,1,67,1111583775,HFLevo,0.0,neutral,nan
+67,1,67,1111583775,LevoParadoxIsomer,-2.0,overconfident,0.5
+68,1,68,243487825,HFLevo,1.3,neutral,nan
+68,1,68,243487825,LevoParadoxIsomer,0.0,neutral,0.5
+69,1,69,1727825584,HFLevo,-0.8,overcautious,nan
+69,1,69,1727825584,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+70,1,70,138221208,HFLevo,0.0,neutral,nan
+70,1,70,138221208,LevoParadoxIsomer,0.0,neutral,0.5
+71,1,71,1644995000,HFLevo,1.2,neutral,nan
+71,1,71,1644995000,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+72,1,72,609184021,HFLevo,0.0,neutral,nan
+72,1,72,609184021,LevoParadoxIsomer,0.0,neutral,0.5
+73,1,73,1231929388,HFLevo,-2.0,overconfident,nan
+73,1,73,1231929388,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+74,1,74,1344075265,HFLevo,1.3,neutral,nan
+74,1,74,1344075265,LevoParadoxIsomer,1.3,neutral,0.5
+75,1,75,1828410100,HFLevo,-2.0,overconfident,nan
+75,1,75,1828410100,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+76,1,76,2048576691,HFLevo,-2.0,overconfident,nan
+76,1,76,2048576691,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+77,1,77,1829711174,HFLevo,1.3,neutral,nan
+77,1,77,1829711174,LevoParadoxIsomer,1.3,neutral,0.5
+78,1,78,993502358,HFLevo,1.3,neutral,nan
+78,1,78,993502358,LevoParadoxIsomer,0.0,neutral,0.5
+79,1,79,796970628,HFLevo,-0.8,overcautious,nan
+79,1,79,796970628,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+80,1,80,1037953041,HFLevo,0.0,neutral,nan
+80,1,80,1037953041,LevoParadoxIsomer,1.3,neutral,0.5
+81,1,81,981620733,HFLevo,0.0,neutral,nan
+81,1,81,981620733,LevoParadoxIsomer,0.0,neutral,0.5
+82,1,82,729569317,HFLevo,0.0,neutral,nan
+82,1,82,729569317,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+83,1,83,646784317,HFLevo,-2.0,overconfident,nan
+83,1,83,646784317,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+84,1,84,740999785,HFLevo,1.3,neutral,nan
+84,1,84,740999785,LevoParadoxIsomer,0.0,neutral,0.5
+85,1,85,1740597160,HFLevo,-2.0,overconfident,nan
+85,1,85,1740597160,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+86,1,86,661906414,HFLevo,1.3,neutral,nan
+86,1,86,661906414,LevoParadoxIsomer,0.0,neutral,0.5
+87,1,87,1060388906,HFLevo,1.3,neutral,nan
+87,1,87,1060388906,LevoParadoxIsomer,0.0,neutral,0.5
+88,1,88,359430184,HFLevo,2.5,neutral,nan
+88,1,88,359430184,LevoParadoxIsomer,0.0,neutral,0.5
+89,1,89,1208987810,HFLevo,1.3,neutral,nan
+89,1,89,1208987810,LevoParadoxIsomer,1.3,neutral,0.5
+90,1,90,2009099257,HFLevo,0.0,neutral,nan
+90,1,90,2009099257,LevoParadoxIsomer,1.3,neutral,0.5
+91,1,91,1034716133,HFLevo,0.0,neutral,nan
+91,1,91,1034716133,LevoParadoxIsomer,1.3,neutral,0.5
+92,1,92,868969428,HFLevo,0.0,neutral,nan
+92,1,92,868969428,LevoParadoxIsomer,-2.0,overconfident,0.3999999761581421
+93,1,93,545077016,HFLevo,2.5,neutral,nan
+93,1,93,545077016,LevoParadoxIsomer,-0.8,overcautious,0.5
+94,1,94,618241786,HFLevo,1.3,neutral,nan
+94,1,94,618241786,LevoParadoxIsomer,0.0,neutral,0.5
+95,1,95,2112109137,HFLevo,2.5,neutral,nan
+95,1,95,2112109137,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+96,1,96,300763006,HFLevo,1.3,neutral,nan
+96,1,96,300763006,LevoParadoxIsomer,0.0,neutral,0.5
+97,1,97,1182747845,HFLevo,0.0,neutral,nan
+97,1,97,1182747845,LevoParadoxIsomer,1.3,neutral,0.5
+98,1,98,997933218,HFLevo,0.0,neutral,nan
+98,1,98,997933218,LevoParadoxIsomer,1.3,neutral,0.5
+99,1,99,2082537479,HFLevo,1.3,neutral,nan
+99,1,99,2082537479,LevoParadoxIsomer,0.0,neutral,0.5
+100,1,100,948374647,HFLevo,0.0,neutral,nan
+100,1,100,948374647,LevoParadoxIsomer,1.3,neutral,0.5
+101,1,101,484158897,HFLevo,1.3,neutral,nan
+101,1,101,484158897,LevoParadoxIsomer,0.0,neutral,0.5
+102,1,102,1023803420,HFLevo,-0.8,overcautious,nan
+102,1,102,1023803420,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+103,1,103,1933155352,HFLevo,0.0,neutral,nan
+103,1,103,1933155352,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+104,1,104,531436601,HFLevo,0.0,neutral,nan
+104,1,104,531436601,LevoParadoxIsomer,0.0,neutral,0.5
+105,1,105,693213698,HFLevo,1.3,neutral,nan
+105,1,105,693213698,LevoParadoxIsomer,0.0,neutral,0.5
+106,1,106,1411219987,HFLevo,-0.8,overcautious,nan
+106,1,106,1411219987,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+107,1,107,707739816,HFLevo,0.0,neutral,nan
+107,1,107,707739816,LevoParadoxIsomer,1.3,neutral,0.5
+108,1,108,7444596,HFLevo,0.0,neutral,nan
+108,1,108,7444596,LevoParadoxIsomer,0.0,neutral,0.5
+109,1,109,1531464733,HFLevo,0.0,neutral,nan
+109,1,109,1531464733,LevoParadoxIsomer,1.3,neutral,0.5
+110,1,110,1407225246,HFLevo,1.3,neutral,nan
+110,1,110,1407225246,LevoParadoxIsomer,0.0,neutral,0.5
+111,1,111,1617971802,HFLevo,2.5,neutral,nan
+111,1,111,1617971802,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+112,1,112,1856904126,HFLevo,0.0,neutral,nan
+112,1,112,1856904126,LevoParadoxIsomer,0.0,neutral,0.5
+113,1,113,2080438781,HFLevo,0.0,neutral,nan
+113,1,113,2080438781,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+114,1,114,92845013,HFLevo,0.0,neutral,nan
+114,1,114,92845013,LevoParadoxIsomer,1.3,neutral,0.5
+115,1,115,2051487433,HFLevo,1.3,neutral,nan
+115,1,115,2051487433,LevoParadoxIsomer,0.0,neutral,0.5
+116,1,116,1625881056,HFLevo,0.0,neutral,nan
+116,1,116,1625881056,LevoParadoxIsomer,0.0,neutral,0.5
+117,1,117,1999467027,HFLevo,0.0,neutral,nan
+117,1,117,1999467027,LevoParadoxIsomer,0.0,neutral,0.5
+118,1,118,759769092,HFLevo,-0.8,overcautious,nan
+118,1,118,759769092,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+119,1,119,1368848130,HFLevo,1.3,neutral,nan
+119,1,119,1368848130,LevoParadoxIsomer,0.0,neutral,0.5
+120,1,120,2079957582,HFLevo,0.0,neutral,nan
+120,1,120,2079957582,LevoParadoxIsomer,0.0,neutral,0.5
+121,1,121,858968924,HFLevo,0.0,neutral,nan
+121,1,121,858968924,LevoParadoxIsomer,1.3,neutral,0.5
+122,1,122,2037278180,HFLevo,1.3,neutral,nan
+122,1,122,2037278180,LevoParadoxIsomer,1.3,neutral,0.5
+123,1,123,301224287,HFLevo,-0.8,overcautious,nan
+123,1,123,301224287,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+124,1,124,595106119,HFLevo,1.3,neutral,nan
+124,1,124,595106119,LevoParadoxIsomer,1.3,neutral,0.5
+125,1,125,830628267,HFLevo,1.3,neutral,nan
+125,1,125,830628267,LevoParadoxIsomer,0.0,neutral,0.5
+126,1,126,2056643707,HFLevo,0.0,neutral,nan
+126,1,126,2056643707,LevoParadoxIsomer,0.0,neutral,0.5
+127,1,127,676826452,HFLevo,0.0,neutral,nan
+127,1,127,676826452,LevoParadoxIsomer,1.3,neutral,0.5
+128,1,128,1391976925,HFLevo,2.5,neutral,nan
+128,1,128,1391976925,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+129,1,129,1312449805,HFLevo,0.0,neutral,nan
+129,1,129,1312449805,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+130,1,130,441735078,HFLevo,0.0,neutral,nan
+130,1,130,441735078,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+131,1,131,1198114699,HFLevo,1.3,neutral,nan
+131,1,131,1198114699,LevoParadoxIsomer,0.0,neutral,0.5
+132,1,132,1177544228,HFLevo,0.0,neutral,nan
+132,1,132,1177544228,LevoParadoxIsomer,0.0,neutral,0.5
+133,1,133,235682355,HFLevo,1.3,neutral,nan
+133,1,133,235682355,LevoParadoxIsomer,1.3,neutral,0.5
+134,1,134,594730155,HFLevo,2.5,neutral,nan
+134,1,134,594730155,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+135,1,135,1031283908,HFLevo,0.0,neutral,nan
+135,1,135,1031283908,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+136,1,136,1468565562,HFLevo,1.3,neutral,nan
+136,1,136,1468565562,LevoParadoxIsomer,0.0,neutral,0.5
+137,1,137,1799515670,HFLevo,-2.0,overconfident,nan
+137,1,137,1799515670,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+138,1,138,1374703813,HFLevo,1.3,neutral,nan
+138,1,138,1374703813,LevoParadoxIsomer,1.3,neutral,0.5
+139,1,139,1412669082,HFLevo,0.0,neutral,nan
+139,1,139,1412669082,LevoParadoxIsomer,0.0,neutral,0.5
+140,1,140,342509991,HFLevo,0.0,neutral,nan
+140,1,140,342509991,LevoParadoxIsomer,0.0,neutral,0.5
+141,1,141,129641700,HFLevo,1.2,neutral,nan
+141,1,141,129641700,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+142,1,142,1979539584,HFLevo,1.2,neutral,nan
+142,1,142,1979539584,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+143,1,143,181563758,HFLevo,1.3,neutral,nan
+143,1,143,181563758,LevoParadoxIsomer,1.3,neutral,0.5
+144,1,144,2121376515,HFLevo,1.3,neutral,nan
+144,1,144,2121376515,LevoParadoxIsomer,1.3,neutral,0.5
+145,1,145,359483640,HFLevo,1.2,neutral,nan
+145,1,145,359483640,LevoParadoxIsomer,-2.0,overconfident,0.34999996423721313
+146,1,146,1009660805,HFLevo,0.0,neutral,nan
+146,1,146,1009660805,LevoParadoxIsomer,0.0,neutral,0.5
+147,1,147,1772095163,HFLevo,2.5,neutral,nan
+147,1,147,1772095163,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+148,1,148,793047113,HFLevo,1.3,neutral,nan
+148,1,148,793047113,LevoParadoxIsomer,0.0,neutral,0.5
+149,1,149,2123812620,HFLevo,1.2,neutral,nan
+149,1,149,2123812620,LevoParadoxIsomer,-2.0,overconfident,0.2999999523162842
+150,1,150,160023209,HFLevo,1.2,neutral,nan
+150,1,150,160023209,LevoParadoxIsomer,-2.0,overconfident,0.24999995529651642
+151,1,151,883833601,HFLevo,1.3,neutral,nan
+151,1,151,883833601,LevoParadoxIsomer,1.3,neutral,0.5
+152,1,152,1345236526,HFLevo,0.0,neutral,nan
+152,1,152,1345236526,LevoParadoxIsomer,0.0,neutral,0.5
+153,1,153,2123563640,HFLevo,0.0,neutral,nan
+153,1,153,2123563640,LevoParadoxIsomer,1.3,neutral,0.5
+154,1,154,1188897582,HFLevo,0.0,neutral,nan
+154,1,154,1188897582,LevoParadoxIsomer,0.0,neutral,0.5
+155,1,155,1835296655,HFLevo,1.3,neutral,nan
+155,1,155,1835296655,LevoParadoxIsomer,0.0,neutral,0.5
+156,1,156,263292300,HFLevo,0.0,neutral,nan
+156,1,156,263292300,LevoParadoxIsomer,1.3,neutral,0.5
+157,1,157,339487389,HFLevo,0.0,neutral,nan
+157,1,157,339487389,LevoParadoxIsomer,0.0,neutral,0.5
+158,1,158,640187192,HFLevo,-0.8,overcautious,nan
+158,1,158,640187192,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+159,1,159,1603617236,HFLevo,0.0,neutral,nan
+159,1,159,1603617236,LevoParadoxIsomer,0.0,neutral,0.5
+160,1,160,437479272,HFLevo,2.5,neutral,nan
+160,1,160,437479272,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+161,1,161,1297938304,HFLevo,0.0,neutral,nan
+161,1,161,1297938304,LevoParadoxIsomer,1.3,neutral,0.5
+162,1,162,1736782144,HFLevo,1.3,neutral,nan
+162,1,162,1736782144,LevoParadoxIsomer,1.3,neutral,0.5
+163,1,163,1188005741,HFLevo,0.0,neutral,nan
+163,1,163,1188005741,LevoParadoxIsomer,1.3,neutral,0.5
+164,1,164,1925962876,HFLevo,1.3,neutral,nan
+164,1,164,1925962876,LevoParadoxIsomer,0.0,neutral,0.5
+165,1,165,1419686978,HFLevo,1.3,neutral,nan
+165,1,165,1419686978,LevoParadoxIsomer,0.0,neutral,0.5
+166,1,166,113085408,HFLevo,0.0,neutral,nan
+166,1,166,113085408,LevoParadoxIsomer,1.3,neutral,0.5
+167,1,167,2107650812,HFLevo,1.3,neutral,nan
+167,1,167,2107650812,LevoParadoxIsomer,0.0,neutral,0.5
+168,1,168,1556544035,HFLevo,1.3,neutral,nan
+168,1,168,1556544035,LevoParadoxIsomer,0.0,neutral,0.5
+169,1,169,409268109,HFLevo,0.0,neutral,nan
+169,1,169,409268109,LevoParadoxIsomer,0.0,neutral,0.5
+170,1,170,1707984479,HFLevo,1.3,neutral,nan
+170,1,170,1707984479,LevoParadoxIsomer,1.3,neutral,0.5
+171,1,171,2028373425,HFLevo,0.0,neutral,nan
+171,1,171,2028373425,LevoParadoxIsomer,1.3,neutral,0.5
+172,1,172,1712063860,HFLevo,1.3,neutral,nan
+172,1,172,1712063860,LevoParadoxIsomer,0.0,neutral,0.5
+173,1,173,729831432,HFLevo,0.0,neutral,nan
+173,1,173,729831432,LevoParadoxIsomer,1.3,neutral,0.5
+174,1,174,1945712103,HFLevo,-0.8,overcautious,nan
+174,1,174,1945712103,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+175,1,175,1662118687,HFLevo,1.3,neutral,nan
+175,1,175,1662118687,LevoParadoxIsomer,0.0,neutral,0.5
+176,1,176,1263277082,HFLevo,1.3,neutral,nan
+176,1,176,1263277082,LevoParadoxIsomer,1.3,neutral,0.5
+177,1,177,600780774,HFLevo,1.3,neutral,nan
+177,1,177,600780774,LevoParadoxIsomer,0.0,neutral,0.5
+178,1,178,389237547,HFLevo,0.0,neutral,nan
+178,1,178,389237547,LevoParadoxIsomer,1.3,neutral,0.5
+179,1,179,531352528,HFLevo,1.3,neutral,nan
+179,1,179,531352528,LevoParadoxIsomer,1.3,neutral,0.5
+180,1,180,308998320,HFLevo,1.2,neutral,nan
+180,1,180,308998320,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+181,1,181,133112608,HFLevo,0.0,neutral,nan
+181,1,181,133112608,LevoParadoxIsomer,0.0,neutral,0.5
+182,1,182,1335500866,HFLevo,0.0,neutral,nan
+182,1,182,1335500866,LevoParadoxIsomer,1.3,neutral,0.5
+183,1,183,1455247303,HFLevo,2.5,neutral,nan
+183,1,183,1455247303,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+184,1,184,818574077,HFLevo,1.2,neutral,nan
+184,1,184,818574077,LevoParadoxIsomer,-2.0,overconfident,0.3999999761581421
+185,1,185,783962675,HFLevo,1.2,neutral,nan
+185,1,185,783962675,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+186,1,186,181722168,HFLevo,-0.8,overcautious,nan
+186,1,186,181722168,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+187,1,187,922465315,HFLevo,1.3,neutral,nan
+187,1,187,922465315,LevoParadoxIsomer,0.0,neutral,0.5
+188,1,188,1058682574,HFLevo,0.0,neutral,nan
+188,1,188,1058682574,LevoParadoxIsomer,0.0,neutral,0.5
+189,1,189,2126397323,HFLevo,1.3,neutral,nan
+189,1,189,2126397323,LevoParadoxIsomer,1.3,neutral,0.5
+190,1,190,23796221,HFLevo,1.2,neutral,nan
+190,1,190,23796221,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+191,1,191,677074079,HFLevo,-0.8,overcautious,nan
+191,1,191,677074079,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+192,1,192,1977263236,HFLevo,1.2,neutral,nan
+192,1,192,1977263236,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+193,1,193,1768875981,HFLevo,0.0,neutral,nan
+193,1,193,1768875981,LevoParadoxIsomer,0.0,neutral,0.5
+194,1,194,1212545188,HFLevo,2.5,neutral,nan
+194,1,194,1212545188,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+195,1,195,1186674390,HFLevo,1.3,neutral,nan
+195,1,195,1186674390,LevoParadoxIsomer,0.0,neutral,0.5
+196,1,196,1056632766,HFLevo,1.3,neutral,nan
+196,1,196,1056632766,LevoParadoxIsomer,1.3,neutral,0.5
+197,1,197,268535661,HFLevo,0.0,neutral,nan
+197,1,197,268535661,LevoParadoxIsomer,1.3,neutral,0.5
+198,1,198,888167157,HFLevo,1.2,neutral,nan
+198,1,198,888167157,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+199,1,199,530391691,HFLevo,0.0,neutral,nan
+199,1,199,530391691,LevoParadoxIsomer,1.3,neutral,0.5
+200,1,200,50594990,HFLevo,2.5,neutral,nan
+200,1,200,50594990,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+201,1,201,222395105,HFLevo,1.2,neutral,nan
+201,1,201,222395105,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+202,1,202,287284573,HFLevo,1.3,neutral,nan
+202,1,202,287284573,LevoParadoxIsomer,1.3,neutral,0.5
+203,1,203,514973304,HFLevo,0.0,neutral,nan
+203,1,203,514973304,LevoParadoxIsomer,0.0,neutral,0.5
+204,1,204,1909475802,HFLevo,2.5,neutral,nan
+204,1,204,1909475802,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+205,1,205,1879525266,HFLevo,-2.0,overconfident,nan
+205,1,205,1879525266,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+206,1,206,1420380137,HFLevo,-0.8,overcautious,nan
+206,1,206,1420380137,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+207,1,207,1737331410,HFLevo,0.0,neutral,nan
+207,1,207,1737331410,LevoParadoxIsomer,1.3,neutral,0.5
+208,1,208,1064923684,HFLevo,1.3,neutral,nan
+208,1,208,1064923684,LevoParadoxIsomer,1.3,neutral,0.5
+209,1,209,1238273317,HFLevo,0.0,neutral,nan
+209,1,209,1238273317,LevoParadoxIsomer,1.3,neutral,0.5
+210,1,210,144185798,HFLevo,0.0,neutral,nan
+210,1,210,144185798,LevoParadoxIsomer,1.3,neutral,0.5
+211,1,211,575162410,HFLevo,1.3,neutral,nan
+211,1,211,575162410,LevoParadoxIsomer,0.0,neutral,0.5
+212,1,212,707283582,HFLevo,2.5,neutral,nan
+212,1,212,707283582,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+213,1,213,1405835406,HFLevo,1.3,neutral,nan
+213,1,213,1405835406,LevoParadoxIsomer,1.3,neutral,0.5
+214,1,214,789235422,HFLevo,1.3,neutral,nan
+214,1,214,789235422,LevoParadoxIsomer,0.0,neutral,0.5
+215,1,215,1388271326,HFLevo,1.3,neutral,nan
+215,1,215,1388271326,LevoParadoxIsomer,1.3,neutral,0.5
+216,1,216,1865718892,HFLevo,0.0,neutral,nan
+216,1,216,1865718892,LevoParadoxIsomer,1.3,neutral,0.5
+217,1,217,1293915651,HFLevo,1.3,neutral,nan
+217,1,217,1293915651,LevoParadoxIsomer,1.3,neutral,0.5
+218,1,218,1428150647,HFLevo,2.5,neutral,nan
+218,1,218,1428150647,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+219,1,219,19248056,HFLevo,2.5,neutral,nan
+219,1,219,19248056,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+220,1,220,491031830,HFLevo,0.0,neutral,nan
+220,1,220,491031830,LevoParadoxIsomer,0.0,neutral,0.5
+221,1,221,636126198,HFLevo,1.3,neutral,nan
+221,1,221,636126198,LevoParadoxIsomer,0.0,neutral,0.5
+222,1,222,456728828,HFLevo,1.2,neutral,nan
+222,1,222,456728828,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+223,1,223,966697912,HFLevo,1.2,neutral,nan
+223,1,223,966697912,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+224,1,224,1166498720,HFLevo,0.0,neutral,nan
+224,1,224,1166498720,LevoParadoxIsomer,1.3,neutral,0.5
+225,1,225,1571014065,HFLevo,0.0,neutral,nan
+225,1,225,1571014065,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+226,1,226,714126436,HFLevo,1.3,neutral,nan
+226,1,226,714126436,LevoParadoxIsomer,0.0,neutral,0.5
+227,1,227,1206418342,HFLevo,1.3,neutral,nan
+227,1,227,1206418342,LevoParadoxIsomer,1.3,neutral,0.5
+228,1,228,1372090007,HFLevo,1.2,neutral,nan
+228,1,228,1372090007,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+229,1,229,769009050,HFLevo,2.5,neutral,nan
+229,1,229,769009050,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+230,1,230,1353001314,HFLevo,0.0,neutral,nan
+230,1,230,1353001314,LevoParadoxIsomer,0.0,neutral,0.5
+231,1,231,1733327736,HFLevo,0.0,neutral,nan
+231,1,231,1733327736,LevoParadoxIsomer,1.3,neutral,0.5
+232,1,232,638285556,HFLevo,1.3,neutral,nan
+232,1,232,638285556,LevoParadoxIsomer,1.3,neutral,0.5
+233,1,233,988454733,HFLevo,1.3,neutral,nan
+233,1,233,988454733,LevoParadoxIsomer,0.0,neutral,0.5
+234,1,234,1459581848,HFLevo,1.3,neutral,nan
+234,1,234,1459581848,LevoParadoxIsomer,0.0,neutral,0.5
+235,1,235,1106839428,HFLevo,1.2,neutral,nan
+235,1,235,1106839428,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+236,1,236,388494700,HFLevo,0.0,neutral,nan
+236,1,236,388494700,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+237,1,237,1441804184,HFLevo,0.0,neutral,nan
+237,1,237,1441804184,LevoParadoxIsomer,0.0,neutral,0.5
+238,1,238,683008548,HFLevo,0.0,neutral,nan
+238,1,238,683008548,LevoParadoxIsomer,1.3,neutral,0.5
+239,1,239,557447257,HFLevo,1.3,neutral,nan
+239,1,239,557447257,LevoParadoxIsomer,1.3,neutral,0.5
+240,1,240,1553434256,HFLevo,1.2,neutral,nan
+240,1,240,1553434256,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+241,1,241,1196216329,HFLevo,0.0,neutral,nan
+241,1,241,1196216329,LevoParadoxIsomer,1.3,neutral,0.5
+242,1,242,205445195,HFLevo,0.0,neutral,nan
+242,1,242,205445195,LevoParadoxIsomer,1.3,neutral,0.5
+243,1,243,138987130,HFLevo,1.3,neutral,nan
+243,1,243,138987130,LevoParadoxIsomer,0.0,neutral,0.5
+244,1,244,1601279830,HFLevo,0.0,neutral,nan
+244,1,244,1601279830,LevoParadoxIsomer,0.0,neutral,0.5
+245,1,245,523497866,HFLevo,2.5,neutral,nan
+245,1,245,523497866,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+246,1,246,1681903654,HFLevo,1.3,neutral,nan
+246,1,246,1681903654,LevoParadoxIsomer,0.0,neutral,0.5
+247,1,247,1447758672,HFLevo,1.3,neutral,nan
+247,1,247,1447758672,LevoParadoxIsomer,0.0,neutral,0.5
+248,1,248,1263090791,HFLevo,1.2,neutral,nan
+248,1,248,1263090791,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+249,1,249,2069934169,HFLevo,1.3,neutral,nan
+249,1,249,2069934169,LevoParadoxIsomer,1.3,neutral,0.5
+250,1,250,1175589659,HFLevo,0.0,neutral,nan
+250,1,250,1175589659,LevoParadoxIsomer,-2.0,overconfident,0.34999996423721313
+251,1,251,792436927,HFLevo,1.3,neutral,nan
+251,1,251,792436927,LevoParadoxIsomer,1.3,neutral,0.5
+252,1,252,77793463,HFLevo,1.3,neutral,nan
+252,1,252,77793463,LevoParadoxIsomer,0.0,neutral,0.5
+253,1,253,1178447001,HFLevo,0.0,neutral,nan
+253,1,253,1178447001,LevoParadoxIsomer,1.3,neutral,0.5
+254,1,254,1867902247,HFLevo,1.3,neutral,nan
+254,1,254,1867902247,LevoParadoxIsomer,0.0,neutral,0.5
+255,1,255,1132483487,HFLevo,0.0,neutral,nan
+255,1,255,1132483487,LevoParadoxIsomer,0.0,neutral,0.5
+256,1,256,1108550737,HFLevo,0.0,neutral,nan
+256,1,256,1108550737,LevoParadoxIsomer,1.3,neutral,0.5
+257,1,257,761845810,HFLevo,0.0,neutral,nan
+257,1,257,761845810,LevoParadoxIsomer,1.3,neutral,0.5
+258,1,258,2026103214,HFLevo,2.5,neutral,nan
+258,1,258,2026103214,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+259,1,259,1320383576,HFLevo,2.5,neutral,nan
+259,1,259,1320383576,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+260,1,260,165278918,HFLevo,0.0,neutral,nan
+260,1,260,165278918,LevoParadoxIsomer,-2.0,overconfident,0.2999999523162842
+261,1,261,1264440670,HFLevo,2.5,neutral,nan
+261,1,261,1264440670,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+262,1,262,859892894,HFLevo,1.3,neutral,nan
+262,1,262,859892894,LevoParadoxIsomer,0.0,neutral,0.5
+263,1,263,411458685,HFLevo,0.0,neutral,nan
+263,1,263,411458685,LevoParadoxIsomer,1.3,neutral,0.5
+264,1,264,1931263994,HFLevo,0.0,neutral,nan
+264,1,264,1931263994,LevoParadoxIsomer,0.0,neutral,0.5
+265,1,265,213511530,HFLevo,2.5,neutral,nan
+265,1,265,213511530,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+266,1,266,1137608613,HFLevo,0.0,neutral,nan
+266,1,266,1137608613,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+267,1,267,843567272,HFLevo,0.0,neutral,nan
+267,1,267,843567272,LevoParadoxIsomer,0.0,neutral,0.5
+268,1,268,11175988,HFLevo,1.3,neutral,nan
+268,1,268,11175988,LevoParadoxIsomer,1.3,neutral,0.5
+269,1,269,687107894,HFLevo,0.0,neutral,nan
+269,1,269,687107894,LevoParadoxIsomer,0.0,neutral,0.5
+270,1,270,291918902,HFLevo,2.5,neutral,nan
+270,1,270,291918902,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+271,1,271,1087591275,HFLevo,2.5,neutral,nan
+271,1,271,1087591275,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+272,1,272,1813734633,HFLevo,2.5,neutral,nan
+272,1,272,1813734633,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+273,1,273,461816730,HFLevo,0.0,neutral,nan
+273,1,273,461816730,LevoParadoxIsomer,1.3,neutral,0.5
+274,1,274,441050625,HFLevo,1.3,neutral,nan
+274,1,274,441050625,LevoParadoxIsomer,1.3,neutral,0.5
+275,1,275,2141182885,HFLevo,1.3,neutral,nan
+275,1,275,2141182885,LevoParadoxIsomer,0.0,neutral,0.5
+276,1,276,749647573,HFLevo,0.0,neutral,nan
+276,1,276,749647573,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+277,1,277,702601389,HFLevo,0.0,neutral,nan
+277,1,277,702601389,LevoParadoxIsomer,0.0,neutral,0.5
+278,1,278,500813039,HFLevo,0.0,neutral,nan
+278,1,278,500813039,LevoParadoxIsomer,1.3,neutral,0.5
+279,1,279,1327850191,HFLevo,1.2,neutral,nan
+279,1,279,1327850191,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+280,1,280,1479353871,HFLevo,-0.8,overcautious,nan
+280,1,280,1479353871,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+281,1,281,875883029,HFLevo,2.5,neutral,nan
+281,1,281,875883029,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+282,1,282,956514865,HFLevo,-2.0,overconfident,nan
+282,1,282,956514865,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+283,1,283,140168808,HFLevo,2.5,neutral,nan
+283,1,283,140168808,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+284,1,284,2023927855,HFLevo,0.0,neutral,nan
+284,1,284,2023927855,LevoParadoxIsomer,1.3,neutral,0.5
+285,1,285,1850097089,HFLevo,0.0,neutral,nan
+285,1,285,1850097089,LevoParadoxIsomer,1.3,neutral,0.5
+286,1,286,366267895,HFLevo,0.0,neutral,nan
+286,1,286,366267895,LevoParadoxIsomer,1.3,neutral,0.5
+287,1,287,833125277,HFLevo,0.0,neutral,nan
+287,1,287,833125277,LevoParadoxIsomer,1.3,neutral,0.5
+288,1,288,1062058470,HFLevo,0.0,neutral,nan
+288,1,288,1062058470,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+289,1,289,755011055,HFLevo,1.3,neutral,nan
+289,1,289,755011055,LevoParadoxIsomer,1.3,neutral,0.5
+290,1,290,1193341725,HFLevo,1.3,neutral,nan
+290,1,290,1193341725,LevoParadoxIsomer,0.0,neutral,0.5
+291,1,291,47566427,HFLevo,0.0,neutral,nan
+291,1,291,47566427,LevoParadoxIsomer,1.3,neutral,0.5
+292,1,292,1480907230,HFLevo,0.0,neutral,nan
+292,1,292,1480907230,LevoParadoxIsomer,1.3,neutral,0.5
+293,1,293,1193747826,HFLevo,1.3,neutral,nan
+293,1,293,1193747826,LevoParadoxIsomer,1.3,neutral,0.5
+294,1,294,188056822,HFLevo,0.0,neutral,nan
+294,1,294,188056822,LevoParadoxIsomer,0.0,neutral,0.5
+295,1,295,2058188731,HFLevo,1.3,neutral,nan
+295,1,295,2058188731,LevoParadoxIsomer,0.0,neutral,0.5
+296,1,296,131903334,HFLevo,-0.8,overcautious,nan
+296,1,296,131903334,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+297,1,297,2023999375,HFLevo,0.0,neutral,nan
+297,1,297,2023999375,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+298,1,298,14284283,HFLevo,0.0,neutral,nan
+298,1,298,14284283,LevoParadoxIsomer,1.3,neutral,0.5
+299,1,299,430728515,HFLevo,0.0,neutral,nan
+299,1,299,430728515,LevoParadoxIsomer,1.3,neutral,0.5
+300,1,300,1773637227,HFLevo,1.3,neutral,nan
+300,1,300,1773637227,LevoParadoxIsomer,1.3,neutral,0.5
+301,1,301,1447643177,HFLevo,0.0,neutral,nan
+301,1,301,1447643177,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+302,1,302,980890604,HFLevo,2.5,neutral,nan
+302,1,302,980890604,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+303,1,303,1738676116,HFLevo,0.0,neutral,nan
+303,1,303,1738676116,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+304,1,304,202567723,HFLevo,0.0,neutral,nan
+304,1,304,202567723,LevoParadoxIsomer,1.3,neutral,0.5
+305,1,305,1153805088,HFLevo,0.0,neutral,nan
+305,1,305,1153805088,LevoParadoxIsomer,-2.0,overconfident,0.24999995529651642
+306,1,306,972971566,HFLevo,-2.0,overconfident,nan
+306,1,306,972971566,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+307,1,307,1472596543,HFLevo,2.5,neutral,nan
+307,1,307,1472596543,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+308,1,308,1472317967,HFLevo,1.3,neutral,nan
+308,1,308,1472317967,LevoParadoxIsomer,1.3,neutral,0.5
+309,1,309,215008918,HFLevo,0.0,neutral,nan
+309,1,309,215008918,LevoParadoxIsomer,0.0,neutral,0.5
+310,1,310,1105771947,HFLevo,1.3,neutral,nan
+310,1,310,1105771947,LevoParadoxIsomer,1.3,neutral,0.5
+311,1,311,652569339,HFLevo,1.3,neutral,nan
+311,1,311,652569339,LevoParadoxIsomer,1.3,neutral,0.5
+312,1,312,1726057777,HFLevo,2.5,neutral,nan
+312,1,312,1726057777,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+313,1,313,1485944499,HFLevo,0.0,neutral,nan
+313,1,313,1485944499,LevoParadoxIsomer,1.3,neutral,0.5
+314,1,314,294205674,HFLevo,1.3,neutral,nan
+314,1,314,294205674,LevoParadoxIsomer,1.3,neutral,0.5
+315,1,315,1016871026,HFLevo,1.2,neutral,nan
+315,1,315,1016871026,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+316,1,316,1168910589,HFLevo,1.3,neutral,nan
+316,1,316,1168910589,LevoParadoxIsomer,0.0,neutral,0.5
+317,1,317,1517292813,HFLevo,1.3,neutral,nan
+317,1,317,1517292813,LevoParadoxIsomer,0.0,neutral,0.5
+318,1,318,2030522066,HFLevo,2.5,neutral,nan
+318,1,318,2030522066,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+319,1,319,959589186,HFLevo,0.0,neutral,nan
+319,1,319,959589186,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+320,1,320,590193953,HFLevo,1.3,neutral,nan
+320,1,320,590193953,LevoParadoxIsomer,1.3,neutral,0.5
+321,1,321,675005705,HFLevo,1.3,neutral,nan
+321,1,321,675005705,LevoParadoxIsomer,1.3,neutral,0.5
+322,1,322,925232956,HFLevo,0.0,neutral,nan
+322,1,322,925232956,LevoParadoxIsomer,0.0,neutral,0.5
+323,1,323,1408472699,HFLevo,2.5,neutral,nan
+323,1,323,1408472699,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+324,1,324,859189252,HFLevo,1.3,neutral,nan
+324,1,324,859189252,LevoParadoxIsomer,0.0,neutral,0.5
+325,1,325,399323533,HFLevo,0.0,neutral,nan
+325,1,325,399323533,LevoParadoxIsomer,0.0,neutral,0.5
+326,1,326,980315658,HFLevo,-2.0,overconfident,nan
+326,1,326,980315658,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+327,1,327,123987181,HFLevo,1.2,neutral,nan
+327,1,327,123987181,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+328,1,328,409996997,HFLevo,1.3,neutral,nan
+328,1,328,409996997,LevoParadoxIsomer,1.3,neutral,0.5
+329,1,329,708892450,HFLevo,1.2,neutral,nan
+329,1,329,708892450,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+330,1,330,1373932302,HFLevo,0.0,neutral,nan
+330,1,330,1373932302,LevoParadoxIsomer,1.3,neutral,0.5
+331,1,331,2077742390,HFLevo,1.3,neutral,nan
+331,1,331,2077742390,LevoParadoxIsomer,1.3,neutral,0.5
+332,1,332,761108960,HFLevo,1.3,neutral,nan
+332,1,332,761108960,LevoParadoxIsomer,0.0,neutral,0.5
+333,1,333,1211512608,HFLevo,1.3,neutral,nan
+333,1,333,1211512608,LevoParadoxIsomer,0.0,neutral,0.5
+334,1,334,1884662523,HFLevo,0.0,neutral,nan
+334,1,334,1884662523,LevoParadoxIsomer,0.0,neutral,0.5
+335,1,335,2002191522,HFLevo,1.3,neutral,nan
+335,1,335,2002191522,LevoParadoxIsomer,1.3,neutral,0.5
+336,1,336,2028646946,HFLevo,0.0,neutral,nan
+336,1,336,2028646946,LevoParadoxIsomer,1.3,neutral,0.5
+337,1,337,589112024,HFLevo,1.3,neutral,nan
+337,1,337,589112024,LevoParadoxIsomer,1.3,neutral,0.5
+338,1,338,610703030,HFLevo,1.3,neutral,nan
+338,1,338,610703030,LevoParadoxIsomer,1.3,neutral,0.5
+339,1,339,972829234,HFLevo,0.0,neutral,nan
+339,1,339,972829234,LevoParadoxIsomer,1.3,neutral,0.5
+340,1,340,465154269,HFLevo,2.5,neutral,nan
+340,1,340,465154269,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+341,1,341,470901255,HFLevo,1.3,neutral,nan
+341,1,341,470901255,LevoParadoxIsomer,1.3,neutral,0.5
+342,1,342,1990213325,HFLevo,1.3,neutral,nan
+342,1,342,1990213325,LevoParadoxIsomer,0.0,neutral,0.5
+343,1,343,1646775850,HFLevo,1.3,neutral,nan
+343,1,343,1646775850,LevoParadoxIsomer,0.0,neutral,0.5
+344,1,344,1057311607,HFLevo,0.0,neutral,nan
+344,1,344,1057311607,LevoParadoxIsomer,1.3,neutral,0.5
+345,1,345,590319545,HFLevo,1.3,neutral,nan
+345,1,345,590319545,LevoParadoxIsomer,0.0,neutral,0.5
+346,1,346,516318123,HFLevo,1.3,neutral,nan
+346,1,346,516318123,LevoParadoxIsomer,0.0,neutral,0.5
+347,1,347,70890343,HFLevo,1.3,neutral,nan
+347,1,347,70890343,LevoParadoxIsomer,1.3,neutral,0.5
+348,1,348,804413128,HFLevo,2.5,neutral,nan
+348,1,348,804413128,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+349,1,349,1504695760,HFLevo,0.0,neutral,nan
+349,1,349,1504695760,LevoParadoxIsomer,1.3,neutral,0.5
+350,1,350,940669613,HFLevo,0.0,neutral,nan
+350,1,350,940669613,LevoParadoxIsomer,0.0,neutral,0.5
+351,1,351,821018278,HFLevo,1.3,neutral,nan
+351,1,351,821018278,LevoParadoxIsomer,1.3,neutral,0.5
+352,1,352,2080007758,HFLevo,2.5,neutral,nan
+352,1,352,2080007758,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+353,1,353,461373511,HFLevo,1.3,neutral,nan
+353,1,353,461373511,LevoParadoxIsomer,0.0,neutral,0.5
+354,1,354,1987269884,HFLevo,1.3,neutral,nan
+354,1,354,1987269884,LevoParadoxIsomer,0.0,neutral,0.5
+355,1,355,885162807,HFLevo,1.3,neutral,nan
+355,1,355,885162807,LevoParadoxIsomer,0.0,neutral,0.5
+356,1,356,779768070,HFLevo,1.3,neutral,nan
+356,1,356,779768070,LevoParadoxIsomer,1.3,neutral,0.5
+357,1,357,966063187,HFLevo,1.2,neutral,nan
+357,1,357,966063187,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+358,1,358,1359588046,HFLevo,1.3,neutral,nan
+358,1,358,1359588046,LevoParadoxIsomer,1.3,neutral,0.5
+359,1,359,393633121,HFLevo,1.3,neutral,nan
+359,1,359,393633121,LevoParadoxIsomer,0.0,neutral,0.5
+360,1,360,991070115,HFLevo,1.3,neutral,nan
+360,1,360,991070115,LevoParadoxIsomer,1.3,neutral,0.5
+361,1,361,1695833246,HFLevo,1.3,neutral,nan
+361,1,361,1695833246,LevoParadoxIsomer,1.3,neutral,0.5
+362,1,362,259218709,HFLevo,1.2,neutral,nan
+362,1,362,259218709,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+363,1,363,1252523270,HFLevo,1.2,neutral,nan
+363,1,363,1252523270,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+364,1,364,618943155,HFLevo,1.3,neutral,nan
+364,1,364,618943155,LevoParadoxIsomer,1.3,neutral,0.5
+365,1,365,1149281050,HFLevo,1.3,neutral,nan
+365,1,365,1149281050,LevoParadoxIsomer,1.3,neutral,0.5
+366,1,366,605383710,HFLevo,1.3,neutral,nan
+366,1,366,605383710,LevoParadoxIsomer,1.3,neutral,0.5
+367,1,367,1156676417,HFLevo,1.2,neutral,nan
+367,1,367,1156676417,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+368,1,368,988215448,HFLevo,1.3,neutral,nan
+368,1,368,988215448,LevoParadoxIsomer,1.3,neutral,0.5
+369,1,369,1630817173,HFLevo,2.5,neutral,nan
+369,1,369,1630817173,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+370,1,370,1326405127,HFLevo,1.3,neutral,nan
+370,1,370,1326405127,LevoParadoxIsomer,1.3,neutral,0.5
+371,1,371,279363521,HFLevo,1.3,neutral,nan
+371,1,371,279363521,LevoParadoxIsomer,0.0,neutral,0.5
+372,1,372,673386525,HFLevo,1.3,neutral,nan
+372,1,372,673386525,LevoParadoxIsomer,1.3,neutral,0.5
+373,1,373,1485190259,HFLevo,0.0,neutral,nan
+373,1,373,1485190259,LevoParadoxIsomer,0.0,neutral,0.5
+374,1,374,757249346,HFLevo,1.3,neutral,nan
+374,1,374,757249346,LevoParadoxIsomer,1.3,neutral,0.5
+375,1,375,2139173341,HFLevo,2.5,neutral,nan
+375,1,375,2139173341,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+376,1,376,1407830714,HFLevo,1.3,neutral,nan
+376,1,376,1407830714,LevoParadoxIsomer,1.3,neutral,0.5
+377,1,377,1328293493,HFLevo,1.2,neutral,nan
+377,1,377,1328293493,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+378,1,378,1561225140,HFLevo,1.3,neutral,nan
+378,1,378,1561225140,LevoParadoxIsomer,1.3,neutral,0.5
+379,1,379,2054053838,HFLevo,2.5,neutral,nan
+379,1,379,2054053838,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+380,1,380,1538665569,HFLevo,1.2,neutral,nan
+380,1,380,1538665569,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+381,1,381,578245576,HFLevo,1.3,neutral,nan
+381,1,381,578245576,LevoParadoxIsomer,0.0,neutral,0.5
+382,1,382,1001750142,HFLevo,1.3,neutral,nan
+382,1,382,1001750142,LevoParadoxIsomer,1.3,neutral,0.5
+383,1,383,247349919,HFLevo,1.3,neutral,nan
+383,1,383,247349919,LevoParadoxIsomer,0.0,neutral,0.5
+384,1,384,1913641746,HFLevo,1.3,neutral,nan
+384,1,384,1913641746,LevoParadoxIsomer,0.0,neutral,0.5
+385,1,385,191896624,HFLevo,2.5,neutral,nan
+385,1,385,191896624,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+386,1,386,358450787,HFLevo,0.0,neutral,nan
+386,1,386,358450787,LevoParadoxIsomer,1.3,neutral,0.5
+387,1,387,1304006934,HFLevo,0.0,neutral,nan
+387,1,387,1304006934,LevoParadoxIsomer,1.3,neutral,0.5
+388,1,388,854476003,HFLevo,0.0,neutral,nan
+388,1,388,854476003,LevoParadoxIsomer,0.0,neutral,0.5
+389,1,389,718810870,HFLevo,1.3,neutral,nan
+389,1,389,718810870,LevoParadoxIsomer,1.3,neutral,0.5
+390,1,390,2087848673,HFLevo,1.3,neutral,nan
+390,1,390,2087848673,LevoParadoxIsomer,1.3,neutral,0.5
+391,1,391,565983639,HFLevo,0.0,neutral,nan
+391,1,391,565983639,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+392,1,392,406083697,HFLevo,1.3,neutral,nan
+392,1,392,406083697,LevoParadoxIsomer,0.0,neutral,0.5
+393,1,393,1812367355,HFLevo,1.3,neutral,nan
+393,1,393,1812367355,LevoParadoxIsomer,0.0,neutral,0.5
+394,1,394,1054994518,HFLevo,0.0,neutral,nan
+394,1,394,1054994518,LevoParadoxIsomer,1.3,neutral,0.5
+395,1,395,235136936,HFLevo,1.3,neutral,nan
+395,1,395,235136936,LevoParadoxIsomer,0.0,neutral,0.5
+396,1,396,111732250,HFLevo,0.0,neutral,nan
+396,1,396,111732250,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+397,1,397,289408596,HFLevo,0.0,neutral,nan
+397,1,397,289408596,LevoParadoxIsomer,1.3,neutral,0.5
+398,1,398,1197540953,HFLevo,1.3,neutral,nan
+398,1,398,1197540953,LevoParadoxIsomer,0.0,neutral,0.5
+399,1,399,34196977,HFLevo,0.0,neutral,nan
+399,1,399,34196977,LevoParadoxIsomer,1.3,neutral,0.5
+400,2,0,2034269964,HFLevo,0.0,neutral,nan
+400,2,0,2034269964,LevoParadoxIsomer,0.8,neutral,0.5
+401,2,1,912146831,HFLevo,2.0,neutral,nan
+401,2,1,912146831,LevoParadoxIsomer,2.0,neutral,0.5
+402,2,2,144856919,HFLevo,1.2,neutral,nan
+402,2,2,144856919,LevoParadoxIsomer,-3.0,overconfident,0.5
+403,2,3,685506823,HFLevo,-3.0,overconfident,nan
+403,2,3,685506823,LevoParadoxIsomer,1.2,neutral,0.5
+404,2,4,1939181862,HFLevo,0.0,neutral,nan
+404,2,4,1939181862,LevoParadoxIsomer,0.0,neutral,0.5
+405,2,5,1502803201,HFLevo,-0.8,overcautious,nan
+405,2,5,1502803201,LevoParadoxIsomer,-0.8,overcautious,0.5
+406,2,6,1504803878,HFLevo,-3.0,overconfident,nan
+406,2,6,1504803878,LevoParadoxIsomer,-3.0,overconfident,0.5
+407,2,7,67883367,HFLevo,-0.8,overcautious,nan
+407,2,7,67883367,LevoParadoxIsomer,-0.8,overcautious,0.5
+408,2,8,235174993,HFLevo,0.0,neutral,nan
+408,2,8,235174993,LevoParadoxIsomer,0.0,neutral,0.5
+409,2,9,1767017071,HFLevo,-0.8,overcautious,nan
+409,2,9,1767017071,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+410,2,10,1195637117,HFLevo,1.2,neutral,nan
+410,2,10,1195637117,LevoParadoxIsomer,-3.0,overconfident,0.5
+411,2,11,2140388521,HFLevo,0.0,neutral,nan
+411,2,11,2140388521,LevoParadoxIsomer,0.8,neutral,0.5
+412,2,12,285128217,HFLevo,-3.0,overconfident,nan
+412,2,12,285128217,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+413,2,13,1297214282,HFLevo,0.8,neutral,nan
+413,2,13,1297214282,LevoParadoxIsomer,0.0,neutral,0.5
+414,2,14,2128926662,HFLevo,0.8,neutral,nan
+414,2,14,2128926662,LevoParadoxIsomer,0.8,neutral,0.5
+415,2,15,1227615478,HFLevo,0.8,neutral,nan
+415,2,15,1227615478,LevoParadoxIsomer,0.0,neutral,0.5
+416,2,16,562820587,HFLevo,0.0,neutral,nan
+416,2,16,562820587,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+417,2,17,1938965903,HFLevo,0.0,neutral,nan
+417,2,17,1938965903,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+418,2,18,1938899583,HFLevo,0.0,neutral,nan
+418,2,18,1938899583,LevoParadoxIsomer,0.0,neutral,0.5
+419,2,19,146416322,HFLevo,2.0,neutral,nan
+419,2,19,146416322,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+420,2,20,2034761172,HFLevo,0.8,neutral,nan
+420,2,20,2034761172,LevoParadoxIsomer,0.0,neutral,0.5
+421,2,21,1531184118,HFLevo,0.0,neutral,nan
+421,2,21,1531184118,LevoParadoxIsomer,0.0,neutral,0.5
+422,2,22,1624823478,HFLevo,1.2,neutral,nan
+422,2,22,1624823478,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+423,2,23,1973982599,HFLevo,-0.8,overcautious,nan
+423,2,23,1973982599,LevoParadoxIsomer,2.0,neutral,0.550000011920929
+424,2,24,1433121745,HFLevo,0.8,neutral,nan
+424,2,24,1433121745,LevoParadoxIsomer,0.8,neutral,0.5
+425,2,25,269942302,HFLevo,0.0,neutral,nan
+425,2,25,269942302,LevoParadoxIsomer,2.0,neutral,0.550000011920929
+426,2,26,662577618,HFLevo,1.2,neutral,nan
+426,2,26,662577618,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+427,2,27,1680792863,HFLevo,0.0,neutral,nan
+427,2,27,1680792863,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+428,2,28,608196857,HFLevo,0.0,neutral,nan
+428,2,28,608196857,LevoParadoxIsomer,0.8,neutral,0.5
+429,2,29,750797946,HFLevo,0.8,neutral,nan
+429,2,29,750797946,LevoParadoxIsomer,0.8,neutral,0.5
+430,2,30,1703067462,HFLevo,0.8,neutral,nan
+430,2,30,1703067462,LevoParadoxIsomer,0.0,neutral,0.5
+431,2,31,354277540,HFLevo,0.0,neutral,nan
+431,2,31,354277540,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+432,2,32,516686757,HFLevo,0.0,neutral,nan
+432,2,32,516686757,LevoParadoxIsomer,0.0,neutral,0.5
+433,2,33,145581260,HFLevo,0.8,neutral,nan
+433,2,33,145581260,LevoParadoxIsomer,0.8,neutral,0.5
+434,2,34,1801241574,HFLevo,2.0,neutral,nan
+434,2,34,1801241574,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+435,2,35,2100309440,HFLevo,2.0,neutral,nan
+435,2,35,2100309440,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+436,2,36,1637824222,HFLevo,0.8,neutral,nan
+436,2,36,1637824222,LevoParadoxIsomer,0.8,neutral,0.5
+437,2,37,1781578254,HFLevo,0.0,neutral,nan
+437,2,37,1781578254,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+438,2,38,774535340,HFLevo,0.8,neutral,nan
+438,2,38,774535340,LevoParadoxIsomer,0.8,neutral,0.5
+439,2,39,1592536280,HFLevo,0.8,neutral,nan
+439,2,39,1592536280,LevoParadoxIsomer,0.8,neutral,0.5
+440,2,40,803715272,HFLevo,0.8,neutral,nan
+440,2,40,803715272,LevoParadoxIsomer,0.0,neutral,0.5
+441,2,41,1295445709,HFLevo,-0.8,overcautious,nan
+441,2,41,1295445709,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+442,2,42,580455628,HFLevo,0.0,neutral,nan
+442,2,42,580455628,LevoParadoxIsomer,0.8,neutral,0.5
+443,2,43,608571353,HFLevo,0.0,neutral,nan
+443,2,43,608571353,LevoParadoxIsomer,0.0,neutral,0.5
+444,2,44,220247617,HFLevo,0.0,neutral,nan
+444,2,44,220247617,LevoParadoxIsomer,0.8,neutral,0.5
+445,2,45,1007502304,HFLevo,0.8,neutral,nan
+445,2,45,1007502304,LevoParadoxIsomer,0.8,neutral,0.5
+446,2,46,1485027697,HFLevo,0.0,neutral,nan
+446,2,46,1485027697,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+447,2,47,308424123,HFLevo,0.8,neutral,nan
+447,2,47,308424123,LevoParadoxIsomer,0.8,neutral,0.5
+448,2,48,1831539103,HFLevo,0.0,neutral,nan
+448,2,48,1831539103,LevoParadoxIsomer,0.0,neutral,0.5
+449,2,49,96205481,HFLevo,0.8,neutral,nan
+449,2,49,96205481,LevoParadoxIsomer,0.0,neutral,0.5
+450,2,50,572983894,HFLevo,0.0,neutral,nan
+450,2,50,572983894,LevoParadoxIsomer,0.8,neutral,0.5
+451,2,51,1559559556,HFLevo,0.8,neutral,nan
+451,2,51,1559559556,LevoParadoxIsomer,0.0,neutral,0.5
+452,2,52,289588363,HFLevo,0.8,neutral,nan
+452,2,52,289588363,LevoParadoxIsomer,0.8,neutral,0.5
+453,2,53,1021080331,HFLevo,0.8,neutral,nan
+453,2,53,1021080331,LevoParadoxIsomer,0.0,neutral,0.5
+454,2,54,43845046,HFLevo,0.8,neutral,nan
+454,2,54,43845046,LevoParadoxIsomer,0.8,neutral,0.5
+455,2,55,1969391451,HFLevo,0.0,neutral,nan
+455,2,55,1969391451,LevoParadoxIsomer,0.0,neutral,0.5
+456,2,56,112141255,HFLevo,0.8,neutral,nan
+456,2,56,112141255,LevoParadoxIsomer,0.8,neutral,0.5
+457,2,57,407044235,HFLevo,0.0,neutral,nan
+457,2,57,407044235,LevoParadoxIsomer,0.0,neutral,0.5
+458,2,58,1413331621,HFLevo,0.8,neutral,nan
+458,2,58,1413331621,LevoParadoxIsomer,0.8,neutral,0.5
+459,2,59,1124438664,HFLevo,2.0,neutral,nan
+459,2,59,1124438664,LevoParadoxIsomer,-0.8,overcautious,0.5
+460,2,60,1823383091,HFLevo,0.0,neutral,nan
+460,2,60,1823383091,LevoParadoxIsomer,0.8,neutral,0.5
+461,2,61,1452420947,HFLevo,1.2,neutral,nan
+461,2,61,1452420947,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+462,2,62,2031250630,HFLevo,1.2,neutral,nan
+462,2,62,2031250630,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+463,2,63,788674418,HFLevo,0.8,neutral,nan
+463,2,63,788674418,LevoParadoxIsomer,0.0,neutral,0.5
+464,2,64,1437390327,HFLevo,0.0,neutral,nan
+464,2,64,1437390327,LevoParadoxIsomer,0.8,neutral,0.5
+465,2,65,879262169,HFLevo,2.0,neutral,nan
+465,2,65,879262169,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+466,2,66,958652521,HFLevo,0.8,neutral,nan
+466,2,66,958652521,LevoParadoxIsomer,0.0,neutral,0.5
+467,2,67,2142602411,HFLevo,0.8,neutral,nan
+467,2,67,2142602411,LevoParadoxIsomer,0.0,neutral,0.5
+468,2,68,59947504,HFLevo,0.8,neutral,nan
+468,2,68,59947504,LevoParadoxIsomer,0.0,neutral,0.5
+469,2,69,384107305,HFLevo,0.0,neutral,nan
+469,2,69,384107305,LevoParadoxIsomer,0.8,neutral,0.5
+470,2,70,1830507375,HFLevo,0.8,neutral,nan
+470,2,70,1830507375,LevoParadoxIsomer,0.8,neutral,0.5
+471,2,71,571519169,HFLevo,0.8,neutral,nan
+471,2,71,571519169,LevoParadoxIsomer,0.0,neutral,0.5
+472,2,72,1626492832,HFLevo,0.8,neutral,nan
+472,2,72,1626492832,LevoParadoxIsomer,0.8,neutral,0.5
+473,2,73,1386853771,HFLevo,0.0,neutral,nan
+473,2,73,1386853771,LevoParadoxIsomer,0.8,neutral,0.5
+474,2,74,1706685627,HFLevo,2.0,neutral,nan
+474,2,74,1706685627,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+475,2,75,1611897310,HFLevo,2.0,neutral,nan
+475,2,75,1611897310,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+476,2,76,1751003019,HFLevo,0.0,neutral,nan
+476,2,76,1751003019,LevoParadoxIsomer,0.8,neutral,0.5
+477,2,77,1422082962,HFLevo,0.0,neutral,nan
+477,2,77,1422082962,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+478,2,78,1883691922,HFLevo,0.0,neutral,nan
+478,2,78,1883691922,LevoParadoxIsomer,0.8,neutral,0.5
+479,2,79,1013721795,HFLevo,0.8,neutral,nan
+479,2,79,1013721795,LevoParadoxIsomer,0.8,neutral,0.5
+480,2,80,820766304,HFLevo,0.8,neutral,nan
+480,2,80,820766304,LevoParadoxIsomer,0.8,neutral,0.5
+481,2,81,292577686,HFLevo,0.8,neutral,nan
+481,2,81,292577686,LevoParadoxIsomer,0.8,neutral,0.5
+482,2,82,1774181236,HFLevo,0.0,neutral,nan
+482,2,82,1774181236,LevoParadoxIsomer,0.0,neutral,0.5
+483,2,83,16477750,HFLevo,0.8,neutral,nan
+483,2,83,16477750,LevoParadoxIsomer,0.0,neutral,0.5
+484,2,84,270709347,HFLevo,0.8,neutral,nan
+484,2,84,270709347,LevoParadoxIsomer,0.0,neutral,0.5
+485,2,85,117649210,HFLevo,0.0,neutral,nan
+485,2,85,117649210,LevoParadoxIsomer,0.8,neutral,0.5
+486,2,86,1315442558,HFLevo,0.0,neutral,nan
+486,2,86,1315442558,LevoParadoxIsomer,0.0,neutral,0.5
+487,2,87,2057676732,HFLevo,0.0,neutral,nan
+487,2,87,2057676732,LevoParadoxIsomer,0.0,neutral,0.5
+488,2,88,211459733,HFLevo,0.8,neutral,nan
+488,2,88,211459733,LevoParadoxIsomer,0.8,neutral,0.5
+489,2,89,91146202,HFLevo,0.8,neutral,nan
+489,2,89,91146202,LevoParadoxIsomer,0.0,neutral,0.5
+490,2,90,588068454,HFLevo,0.8,neutral,nan
+490,2,90,588068454,LevoParadoxIsomer,0.0,neutral,0.5
+491,2,91,467526601,HFLevo,2.0,neutral,nan
+491,2,91,467526601,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+492,2,92,335299668,HFLevo,0.0,neutral,nan
+492,2,92,335299668,LevoParadoxIsomer,0.0,neutral,0.5
+493,2,93,779362037,HFLevo,0.8,neutral,nan
+493,2,93,779362037,LevoParadoxIsomer,0.0,neutral,0.5
+494,2,94,660339976,HFLevo,2.0,neutral,nan
+494,2,94,660339976,LevoParadoxIsomer,2.0,neutral,0.6000000238418579
+495,2,95,637136818,HFLevo,1.2,neutral,nan
+495,2,95,637136818,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+496,2,96,1999866855,HFLevo,0.8,neutral,nan
+496,2,96,1999866855,LevoParadoxIsomer,0.0,neutral,0.5
+497,2,97,139896496,HFLevo,0.8,neutral,nan
+497,2,97,139896496,LevoParadoxIsomer,0.8,neutral,0.5
+498,2,98,863743810,HFLevo,0.0,neutral,nan
+498,2,98,863743810,LevoParadoxIsomer,0.0,neutral,0.5
+499,2,99,1908673249,HFLevo,2.0,neutral,nan
+499,2,99,1908673249,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+500,2,100,1039141574,HFLevo,0.0,neutral,nan
+500,2,100,1039141574,LevoParadoxIsomer,0.8,neutral,0.5
+501,2,101,1051567495,HFLevo,1.2,neutral,nan
+501,2,101,1051567495,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+502,2,102,837394817,HFLevo,0.8,neutral,nan
+502,2,102,837394817,LevoParadoxIsomer,0.8,neutral,0.5
+503,2,103,2013481887,HFLevo,0.0,neutral,nan
+503,2,103,2013481887,LevoParadoxIsomer,0.8,neutral,0.5
+504,2,104,438355621,HFLevo,0.8,neutral,nan
+504,2,104,438355621,LevoParadoxIsomer,0.0,neutral,0.5
+505,2,105,993080718,HFLevo,0.0,neutral,nan
+505,2,105,993080718,LevoParadoxIsomer,0.8,neutral,0.5
+506,2,106,1682379032,HFLevo,0.0,neutral,nan
+506,2,106,1682379032,LevoParadoxIsomer,0.8,neutral,0.5
+507,2,107,1013432362,HFLevo,0.8,neutral,nan
+507,2,107,1013432362,LevoParadoxIsomer,0.0,neutral,0.5
+508,2,108,1856562955,HFLevo,0.0,neutral,nan
+508,2,108,1856562955,LevoParadoxIsomer,0.0,neutral,0.5
+509,2,109,1667805176,HFLevo,1.2,neutral,nan
+509,2,109,1667805176,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+510,2,110,506300911,HFLevo,0.0,neutral,nan
+510,2,110,506300911,LevoParadoxIsomer,0.8,neutral,0.5
+511,2,111,222604131,HFLevo,0.8,neutral,nan
+511,2,111,222604131,LevoParadoxIsomer,0.0,neutral,0.5
+512,2,112,804599567,HFLevo,0.0,neutral,nan
+512,2,112,804599567,LevoParadoxIsomer,0.8,neutral,0.5
+513,2,113,1492613445,HFLevo,0.0,neutral,nan
+513,2,113,1492613445,LevoParadoxIsomer,0.8,neutral,0.5
+514,2,114,552650077,HFLevo,1.2,neutral,nan
+514,2,114,552650077,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+515,2,115,930025102,HFLevo,0.0,neutral,nan
+515,2,115,930025102,LevoParadoxIsomer,0.8,neutral,0.5
+516,2,116,873459449,HFLevo,-0.8,overcautious,nan
+516,2,116,873459449,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+517,2,117,467662889,HFLevo,0.0,neutral,nan
+517,2,117,467662889,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+518,2,118,445679122,HFLevo,0.0,neutral,nan
+518,2,118,445679122,LevoParadoxIsomer,0.0,neutral,0.5
+519,2,119,1328599419,HFLevo,2.0,neutral,nan
+519,2,119,1328599419,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+520,2,120,1323806708,HFLevo,0.0,neutral,nan
+520,2,120,1323806708,LevoParadoxIsomer,0.0,neutral,0.5
+521,2,121,1034589592,HFLevo,0.0,neutral,nan
+521,2,121,1034589592,LevoParadoxIsomer,0.8,neutral,0.5
+522,2,122,413811598,HFLevo,0.0,neutral,nan
+522,2,122,413811598,LevoParadoxIsomer,0.8,neutral,0.5
+523,2,123,753550936,HFLevo,0.0,neutral,nan
+523,2,123,753550936,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+524,2,124,1202521986,HFLevo,0.8,neutral,nan
+524,2,124,1202521986,LevoParadoxIsomer,0.8,neutral,0.5
+525,2,125,1933381577,HFLevo,-0.8,overcautious,nan
+525,2,125,1933381577,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+526,2,126,1963329040,HFLevo,0.0,neutral,nan
+526,2,126,1963329040,LevoParadoxIsomer,0.8,neutral,0.5
+527,2,127,1399821071,HFLevo,2.0,neutral,nan
+527,2,127,1399821071,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+528,2,128,1828929971,HFLevo,-3.0,overconfident,nan
+528,2,128,1828929971,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+529,2,129,1100686433,HFLevo,0.0,neutral,nan
+529,2,129,1100686433,LevoParadoxIsomer,0.8,neutral,0.5
+530,2,130,423774818,HFLevo,0.0,neutral,nan
+530,2,130,423774818,LevoParadoxIsomer,0.0,neutral,0.5
+531,2,131,2047001195,HFLevo,0.0,neutral,nan
+531,2,131,2047001195,LevoParadoxIsomer,0.8,neutral,0.5
+532,2,132,127060851,HFLevo,0.8,neutral,nan
+532,2,132,127060851,LevoParadoxIsomer,0.0,neutral,0.5
+533,2,133,1430942851,HFLevo,1.2,neutral,nan
+533,2,133,1430942851,LevoParadoxIsomer,-3.0,overconfident,0.2999999523162842
+534,2,134,1194133788,HFLevo,2.0,neutral,nan
+534,2,134,1194133788,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+535,2,135,30246456,HFLevo,0.0,neutral,nan
+535,2,135,30246456,LevoParadoxIsomer,0.0,neutral,0.5
+536,2,136,1871301169,HFLevo,0.8,neutral,nan
+536,2,136,1871301169,LevoParadoxIsomer,0.0,neutral,0.5
+537,2,137,1724977905,HFLevo,1.2,neutral,nan
+537,2,137,1724977905,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+538,2,138,1717796771,HFLevo,0.0,neutral,nan
+538,2,138,1717796771,LevoParadoxIsomer,0.8,neutral,0.5
+539,2,139,1593858573,HFLevo,0.8,neutral,nan
+539,2,139,1593858573,LevoParadoxIsomer,0.0,neutral,0.5
+540,2,140,200663940,HFLevo,1.2,neutral,nan
+540,2,140,200663940,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+541,2,141,1366649604,HFLevo,0.0,neutral,nan
+541,2,141,1366649604,LevoParadoxIsomer,0.8,neutral,0.5
+542,2,142,1673519505,HFLevo,0.8,neutral,nan
+542,2,142,1673519505,LevoParadoxIsomer,0.0,neutral,0.5
+543,2,143,1177626327,HFLevo,0.8,neutral,nan
+543,2,143,1177626327,LevoParadoxIsomer,0.0,neutral,0.5
+544,2,144,262855495,HFLevo,0.8,neutral,nan
+544,2,144,262855495,LevoParadoxIsomer,0.8,neutral,0.5
+545,2,145,1035690989,HFLevo,-3.0,overconfident,nan
+545,2,145,1035690989,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+546,2,146,192999926,HFLevo,0.8,neutral,nan
+546,2,146,192999926,LevoParadoxIsomer,0.8,neutral,0.5
+547,2,147,1097961901,HFLevo,-0.8,overcautious,nan
+547,2,147,1097961901,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+548,2,148,1040646467,HFLevo,2.0,neutral,nan
+548,2,148,1040646467,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+549,2,149,1995566658,HFLevo,0.8,neutral,nan
+549,2,149,1995566658,LevoParadoxIsomer,0.8,neutral,0.5
+550,2,150,390674833,HFLevo,0.0,neutral,nan
+550,2,150,390674833,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+551,2,151,662645968,HFLevo,0.0,neutral,nan
+551,2,151,662645968,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+552,2,152,111224037,HFLevo,2.0,neutral,nan
+552,2,152,111224037,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+553,2,153,1242319339,HFLevo,0.8,neutral,nan
+553,2,153,1242319339,LevoParadoxIsomer,0.8,neutral,0.5
+554,2,154,1295579201,HFLevo,0.0,neutral,nan
+554,2,154,1295579201,LevoParadoxIsomer,0.0,neutral,0.5
+555,2,155,88079789,HFLevo,0.8,neutral,nan
+555,2,155,88079789,LevoParadoxIsomer,0.8,neutral,0.5
+556,2,156,1218832160,HFLevo,0.8,neutral,nan
+556,2,156,1218832160,LevoParadoxIsomer,0.0,neutral,0.5
+557,2,157,1199450774,HFLevo,0.0,neutral,nan
+557,2,157,1199450774,LevoParadoxIsomer,0.8,neutral,0.5
+558,2,158,93920744,HFLevo,0.8,neutral,nan
+558,2,158,93920744,LevoParadoxIsomer,0.0,neutral,0.5
+559,2,159,301211568,HFLevo,0.0,neutral,nan
+559,2,159,301211568,LevoParadoxIsomer,0.0,neutral,0.5
+560,2,160,606831608,HFLevo,0.0,neutral,nan
+560,2,160,606831608,LevoParadoxIsomer,0.8,neutral,0.5
+561,2,161,248267762,HFLevo,2.0,neutral,nan
+561,2,161,248267762,LevoParadoxIsomer,0.0,neutral,0.7500000596046448
+562,2,162,479831997,HFLevo,0.0,neutral,nan
+562,2,162,479831997,LevoParadoxIsomer,0.8,neutral,0.5
+563,2,163,443794762,HFLevo,-3.0,overconfident,nan
+563,2,163,443794762,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+564,2,164,317845088,HFLevo,0.8,neutral,nan
+564,2,164,317845088,LevoParadoxIsomer,0.8,neutral,0.5
+565,2,165,976368810,HFLevo,-3.0,overconfident,nan
+565,2,165,976368810,LevoParadoxIsomer,0.0,neutral,0.24999995529651642
+566,2,166,1096060710,HFLevo,1.2,neutral,nan
+566,2,166,1096060710,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+567,2,167,1786432503,HFLevo,0.0,neutral,nan
+567,2,167,1786432503,LevoParadoxIsomer,0.0,neutral,0.5
+568,2,168,92915493,HFLevo,0.8,neutral,nan
+568,2,168,92915493,LevoParadoxIsomer,0.0,neutral,0.5
+569,2,169,169421372,HFLevo,0.8,neutral,nan
+569,2,169,169421372,LevoParadoxIsomer,0.8,neutral,0.5
+570,2,170,827738447,HFLevo,2.0,neutral,nan
+570,2,170,827738447,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+571,2,171,91871503,HFLevo,0.0,neutral,nan
+571,2,171,91871503,LevoParadoxIsomer,0.8,neutral,0.5
+572,2,172,1181786707,HFLevo,1.2,neutral,nan
+572,2,172,1181786707,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+573,2,173,14354338,HFLevo,0.0,neutral,nan
+573,2,173,14354338,LevoParadoxIsomer,0.0,neutral,0.5
+574,2,174,2140443672,HFLevo,-3.0,overconfident,nan
+574,2,174,2140443672,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+575,2,175,318790932,HFLevo,2.0,neutral,nan
+575,2,175,318790932,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+576,2,176,721850175,HFLevo,-0.8,overcautious,nan
+576,2,176,721850175,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+577,2,177,1030785601,HFLevo,0.0,neutral,nan
+577,2,177,1030785601,LevoParadoxIsomer,0.0,neutral,0.5
+578,2,178,1316251630,HFLevo,-0.8,overcautious,nan
+578,2,178,1316251630,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+579,2,179,1719447051,HFLevo,0.8,neutral,nan
+579,2,179,1719447051,LevoParadoxIsomer,0.0,neutral,0.5
+580,2,180,574594100,HFLevo,0.0,neutral,nan
+580,2,180,574594100,LevoParadoxIsomer,0.0,neutral,0.5
+581,2,181,2130613154,HFLevo,0.0,neutral,nan
+581,2,181,2130613154,LevoParadoxIsomer,0.0,neutral,0.5
+582,2,182,527701015,HFLevo,0.0,neutral,nan
+582,2,182,527701015,LevoParadoxIsomer,0.0,neutral,0.5
+583,2,183,1227208042,HFLevo,1.2,neutral,nan
+583,2,183,1227208042,LevoParadoxIsomer,0.0,neutral,0.2999999523162842
+584,2,184,633972706,HFLevo,0.0,neutral,nan
+584,2,184,633972706,LevoParadoxIsomer,0.8,neutral,0.5
+585,2,185,1481264686,HFLevo,0.0,neutral,nan
+585,2,185,1481264686,LevoParadoxIsomer,0.0,neutral,0.5
+586,2,186,1403332419,HFLevo,0.0,neutral,nan
+586,2,186,1403332419,LevoParadoxIsomer,0.0,neutral,0.5
+587,2,187,573328882,HFLevo,1.2,neutral,nan
+587,2,187,573328882,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+588,2,188,896430961,HFLevo,0.0,neutral,nan
+588,2,188,896430961,LevoParadoxIsomer,0.8,neutral,0.5
+589,2,189,861308429,HFLevo,0.0,neutral,nan
+589,2,189,861308429,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+590,2,190,857542805,HFLevo,1.2,neutral,nan
+590,2,190,857542805,LevoParadoxIsomer,-3.0,overconfident,0.2999999523162842
+591,2,191,555627012,HFLevo,0.8,neutral,nan
+591,2,191,555627012,LevoParadoxIsomer,0.0,neutral,0.5
+592,2,192,883473089,HFLevo,1.2,neutral,nan
+592,2,192,883473089,LevoParadoxIsomer,-3.0,overconfident,0.24999995529651642
+593,2,193,1183906577,HFLevo,2.0,neutral,nan
+593,2,193,1183906577,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+594,2,194,1295404722,HFLevo,2.0,neutral,nan
+594,2,194,1295404722,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+595,2,195,1602782174,HFLevo,0.0,neutral,nan
+595,2,195,1602782174,LevoParadoxIsomer,0.0,neutral,0.5
+596,2,196,1168170146,HFLevo,0.8,neutral,nan
+596,2,196,1168170146,LevoParadoxIsomer,0.8,neutral,0.5
+597,2,197,247049848,HFLevo,0.0,neutral,nan
+597,2,197,247049848,LevoParadoxIsomer,0.0,neutral,0.5
+598,2,198,1217521579,HFLevo,2.0,neutral,nan
+598,2,198,1217521579,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+599,2,199,1154221074,HFLevo,1.2,neutral,nan
+599,2,199,1154221074,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+600,2,200,1039822287,HFLevo,0.0,neutral,nan
+600,2,200,1039822287,LevoParadoxIsomer,0.0,neutral,0.5
+601,2,201,403774715,HFLevo,0.8,neutral,nan
+601,2,201,403774715,LevoParadoxIsomer,0.0,neutral,0.5
+602,2,202,1937799744,HFLevo,0.8,neutral,nan
+602,2,202,1937799744,LevoParadoxIsomer,0.0,neutral,0.5
+603,2,203,1530207800,HFLevo,0.8,neutral,nan
+603,2,203,1530207800,LevoParadoxIsomer,0.8,neutral,0.5
+604,2,204,899360822,HFLevo,0.8,neutral,nan
+604,2,204,899360822,LevoParadoxIsomer,0.8,neutral,0.5
+605,2,205,839140609,HFLevo,2.0,neutral,nan
+605,2,205,839140609,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+606,2,206,2141035731,HFLevo,2.0,neutral,nan
+606,2,206,2141035731,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+607,2,207,778084307,HFLevo,1.2,neutral,nan
+607,2,207,778084307,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+608,2,208,350945807,HFLevo,0.0,neutral,nan
+608,2,208,350945807,LevoParadoxIsomer,0.0,neutral,0.5
+609,2,209,970046052,HFLevo,2.0,neutral,nan
+609,2,209,970046052,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+610,2,210,828658713,HFLevo,1.2,neutral,nan
+610,2,210,828658713,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+611,2,211,899193899,HFLevo,0.8,neutral,nan
+611,2,211,899193899,LevoParadoxIsomer,0.0,neutral,0.5
+612,2,212,1918263815,HFLevo,1.2,neutral,nan
+612,2,212,1918263815,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+613,2,213,861402774,HFLevo,0.0,neutral,nan
+613,2,213,861402774,LevoParadoxIsomer,0.0,neutral,0.5
+614,2,214,545630730,HFLevo,0.0,neutral,nan
+614,2,214,545630730,LevoParadoxIsomer,0.8,neutral,0.5
+615,2,215,1771344806,HFLevo,0.0,neutral,nan
+615,2,215,1771344806,LevoParadoxIsomer,0.0,neutral,0.5
+616,2,216,835556689,HFLevo,2.0,neutral,nan
+616,2,216,835556689,LevoParadoxIsomer,-0.8,overcautious,0.7500000596046448
+617,2,217,920739262,HFLevo,0.8,neutral,nan
+617,2,217,920739262,LevoParadoxIsomer,0.8,neutral,0.5
+618,2,218,1075133249,HFLevo,0.8,neutral,nan
+618,2,218,1075133249,LevoParadoxIsomer,0.8,neutral,0.5
+619,2,219,1466130915,HFLevo,0.8,neutral,nan
+619,2,219,1466130915,LevoParadoxIsomer,0.0,neutral,0.5
+620,2,220,1545638251,HFLevo,0.0,neutral,nan
+620,2,220,1545638251,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+621,2,221,379504837,HFLevo,0.0,neutral,nan
+621,2,221,379504837,LevoParadoxIsomer,0.8,neutral,0.5
+622,2,222,833504022,HFLevo,0.8,neutral,nan
+622,2,222,833504022,LevoParadoxIsomer,0.8,neutral,0.5
+623,2,223,1846676001,HFLevo,1.2,neutral,nan
+623,2,223,1846676001,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+624,2,224,6416948,HFLevo,0.0,neutral,nan
+624,2,224,6416948,LevoParadoxIsomer,0.0,neutral,0.5
+625,2,225,1751117666,HFLevo,0.0,neutral,nan
+625,2,225,1751117666,LevoParadoxIsomer,0.8,neutral,0.5
+626,2,226,284570123,HFLevo,0.0,neutral,nan
+626,2,226,284570123,LevoParadoxIsomer,0.8,neutral,0.5
+627,2,227,189352376,HFLevo,0.0,neutral,nan
+627,2,227,189352376,LevoParadoxIsomer,0.8,neutral,0.5
+628,2,228,1370043294,HFLevo,0.0,neutral,nan
+628,2,228,1370043294,LevoParadoxIsomer,0.8,neutral,0.5
+629,2,229,1967776583,HFLevo,0.0,neutral,nan
+629,2,229,1967776583,LevoParadoxIsomer,0.8,neutral,0.5
+630,2,230,1013942334,HFLevo,0.8,neutral,nan
+630,2,230,1013942334,LevoParadoxIsomer,0.8,neutral,0.5
+631,2,231,600184202,HFLevo,0.0,neutral,nan
+631,2,231,600184202,LevoParadoxIsomer,0.0,neutral,0.5
+632,2,232,2085636407,HFLevo,0.8,neutral,nan
+632,2,232,2085636407,LevoParadoxIsomer,0.8,neutral,0.5
+633,2,233,328848547,HFLevo,0.8,neutral,nan
+633,2,233,328848547,LevoParadoxIsomer,0.0,neutral,0.5
+634,2,234,63425983,HFLevo,2.0,neutral,nan
+634,2,234,63425983,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+635,2,235,531264304,HFLevo,0.0,neutral,nan
+635,2,235,531264304,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+636,2,236,1531168957,HFLevo,0.8,neutral,nan
+636,2,236,1531168957,LevoParadoxIsomer,0.8,neutral,0.5
+637,2,237,1789312949,HFLevo,2.0,neutral,nan
+637,2,237,1789312949,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+638,2,238,1084402395,HFLevo,0.8,neutral,nan
+638,2,238,1084402395,LevoParadoxIsomer,0.0,neutral,0.5
+639,2,239,1545622175,HFLevo,0.8,neutral,nan
+639,2,239,1545622175,LevoParadoxIsomer,0.8,neutral,0.5
+640,2,240,999431888,HFLevo,0.0,neutral,nan
+640,2,240,999431888,LevoParadoxIsomer,0.8,neutral,0.5
+641,2,241,1725426490,HFLevo,0.8,neutral,nan
+641,2,241,1725426490,LevoParadoxIsomer,0.8,neutral,0.5
+642,2,242,669407095,HFLevo,0.8,neutral,nan
+642,2,242,669407095,LevoParadoxIsomer,0.8,neutral,0.5
+643,2,243,1417993163,HFLevo,0.0,neutral,nan
+643,2,243,1417993163,LevoParadoxIsomer,0.0,neutral,0.5
+644,2,244,1057045593,HFLevo,0.0,neutral,nan
+644,2,244,1057045593,LevoParadoxIsomer,0.8,neutral,0.5
+645,2,245,1380890934,HFLevo,0.8,neutral,nan
+645,2,245,1380890934,LevoParadoxIsomer,0.0,neutral,0.5
+646,2,246,238129855,HFLevo,0.0,neutral,nan
+646,2,246,238129855,LevoParadoxIsomer,0.8,neutral,0.5
+647,2,247,1486485713,HFLevo,0.8,neutral,nan
+647,2,247,1486485713,LevoParadoxIsomer,0.8,neutral,0.5
+648,2,248,30716145,HFLevo,0.0,neutral,nan
+648,2,248,30716145,LevoParadoxIsomer,0.8,neutral,0.5
+649,2,249,1615261593,HFLevo,0.0,neutral,nan
+649,2,249,1615261593,LevoParadoxIsomer,0.0,neutral,0.5
+650,2,250,2052819092,HFLevo,0.0,neutral,nan
+650,2,250,2052819092,LevoParadoxIsomer,0.8,neutral,0.5
+651,2,251,11185487,HFLevo,0.8,neutral,nan
+651,2,251,11185487,LevoParadoxIsomer,0.8,neutral,0.5
+652,2,252,928000143,HFLevo,-0.8,overcautious,nan
+652,2,252,928000143,LevoParadoxIsomer,-0.8,overcautious,0.8000000715255737
+653,2,253,2023963770,HFLevo,-3.0,overconfident,nan
+653,2,253,2023963770,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+654,2,254,1301736393,HFLevo,0.0,neutral,nan
+654,2,254,1301736393,LevoParadoxIsomer,0.8,neutral,0.5
+655,2,255,398352915,HFLevo,0.8,neutral,nan
+655,2,255,398352915,LevoParadoxIsomer,0.8,neutral,0.5
+656,2,256,299104752,HFLevo,0.8,neutral,nan
+656,2,256,299104752,LevoParadoxIsomer,0.8,neutral,0.5
+657,2,257,1900619662,HFLevo,2.0,neutral,nan
+657,2,257,1900619662,LevoParadoxIsomer,2.0,neutral,0.8500000834465027
+658,2,258,402834001,HFLevo,0.0,neutral,nan
+658,2,258,402834001,LevoParadoxIsomer,-3.0,overconfident,0.24999995529651642
+659,2,259,618191185,HFLevo,-3.0,overconfident,nan
+659,2,259,618191185,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+660,2,260,1107226955,HFLevo,2.0,neutral,nan
+660,2,260,1107226955,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+661,2,261,1079996997,HFLevo,0.8,neutral,nan
+661,2,261,1079996997,LevoParadoxIsomer,0.0,neutral,0.5
+662,2,262,1090799708,HFLevo,-0.8,overcautious,nan
+662,2,262,1090799708,LevoParadoxIsomer,2.0,neutral,0.8500000834465027
+663,2,263,614807561,HFLevo,1.2,neutral,nan
+663,2,263,614807561,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+664,2,264,1110540374,HFLevo,0.0,neutral,nan
+664,2,264,1110540374,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+665,2,265,230358128,HFLevo,0.0,neutral,nan
+665,2,265,230358128,LevoParadoxIsomer,0.0,neutral,0.5
+666,2,266,1307025172,HFLevo,0.8,neutral,nan
+666,2,266,1307025172,LevoParadoxIsomer,0.8,neutral,0.5
+667,2,267,2128513208,HFLevo,0.0,neutral,nan
+667,2,267,2128513208,LevoParadoxIsomer,-3.0,overconfident,0.19999995827674866
+668,2,268,1813190214,HFLevo,0.0,neutral,nan
+668,2,268,1813190214,LevoParadoxIsomer,-3.0,overconfident,0.19999995827674866
+669,2,269,427517589,HFLevo,2.0,neutral,nan
+669,2,269,427517589,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+670,2,270,971704768,HFLevo,0.0,neutral,nan
+670,2,270,971704768,LevoParadoxIsomer,0.8,neutral,0.5
+671,2,271,379120844,HFLevo,0.8,neutral,nan
+671,2,271,379120844,LevoParadoxIsomer,0.8,neutral,0.5
+672,2,272,366674027,HFLevo,2.0,neutral,nan
+672,2,272,366674027,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+673,2,273,1267589757,HFLevo,0.0,neutral,nan
+673,2,273,1267589757,LevoParadoxIsomer,0.0,neutral,0.5
+674,2,274,397609752,HFLevo,0.0,neutral,nan
+674,2,274,397609752,LevoParadoxIsomer,0.0,neutral,0.5
+675,2,275,544583423,HFLevo,0.8,neutral,nan
+675,2,275,544583423,LevoParadoxIsomer,0.0,neutral,0.5
+676,2,276,1765101426,HFLevo,0.0,neutral,nan
+676,2,276,1765101426,LevoParadoxIsomer,0.8,neutral,0.5
+677,2,277,569693597,HFLevo,2.0,neutral,nan
+677,2,277,569693597,LevoParadoxIsomer,-0.8,overcautious,0.8500000834465027
+678,2,278,189460983,HFLevo,0.0,neutral,nan
+678,2,278,189460983,LevoParadoxIsomer,0.0,neutral,0.5
+679,2,279,505940532,HFLevo,0.8,neutral,nan
+679,2,279,505940532,LevoParadoxIsomer,0.0,neutral,0.5
+680,2,280,961700536,HFLevo,-3.0,overconfident,nan
+680,2,280,961700536,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+681,2,281,932456279,HFLevo,2.0,neutral,nan
+681,2,281,932456279,LevoParadoxIsomer,-0.8,overcautious,0.7500000596046448
+682,2,282,390287427,HFLevo,2.0,neutral,nan
+682,2,282,390287427,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+683,2,283,297976648,HFLevo,2.0,neutral,nan
+683,2,283,297976648,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+684,2,284,436098708,HFLevo,0.8,neutral,nan
+684,2,284,436098708,LevoParadoxIsomer,0.0,neutral,0.5
+685,2,285,794629335,HFLevo,2.0,neutral,nan
+685,2,285,794629335,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+686,2,286,1214082779,HFLevo,0.0,neutral,nan
+686,2,286,1214082779,LevoParadoxIsomer,0.8,neutral,0.5
+687,2,287,1935214602,HFLevo,2.0,neutral,nan
+687,2,287,1935214602,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+688,2,288,284248471,HFLevo,0.8,neutral,nan
+688,2,288,284248471,LevoParadoxIsomer,0.0,neutral,0.5
+689,2,289,1230641234,HFLevo,0.0,neutral,nan
+689,2,289,1230641234,LevoParadoxIsomer,0.8,neutral,0.5
+690,2,290,388858966,HFLevo,2.0,neutral,nan
+690,2,290,388858966,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+691,2,291,1021791854,HFLevo,2.0,neutral,nan
+691,2,291,1021791854,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+692,2,292,603303402,HFLevo,0.0,neutral,nan
+692,2,292,603303402,LevoParadoxIsomer,0.8,neutral,0.5
+693,2,293,862608619,HFLevo,0.0,neutral,nan
+693,2,293,862608619,LevoParadoxIsomer,0.0,neutral,0.5
+694,2,294,1651871421,HFLevo,0.8,neutral,nan
+694,2,294,1651871421,LevoParadoxIsomer,0.0,neutral,0.5
+695,2,295,2051444313,HFLevo,0.0,neutral,nan
+695,2,295,2051444313,LevoParadoxIsomer,0.8,neutral,0.5
+696,2,296,144628230,HFLevo,0.0,neutral,nan
+696,2,296,144628230,LevoParadoxIsomer,0.8,neutral,0.5
+697,2,297,1520429634,HFLevo,0.8,neutral,nan
+697,2,297,1520429634,LevoParadoxIsomer,0.0,neutral,0.5
+698,2,298,881109934,HFLevo,0.0,neutral,nan
+698,2,298,881109934,LevoParadoxIsomer,0.8,neutral,0.5
+699,2,299,1255956995,HFLevo,0.0,neutral,nan
+699,2,299,1255956995,LevoParadoxIsomer,0.0,neutral,0.5
+700,2,300,208230768,HFLevo,0.8,neutral,nan
+700,2,300,208230768,LevoParadoxIsomer,0.8,neutral,0.5
+701,2,301,1460257335,HFLevo,0.0,neutral,nan
+701,2,301,1460257335,LevoParadoxIsomer,0.0,neutral,0.5
+702,2,302,1011782590,HFLevo,0.8,neutral,nan
+702,2,302,1011782590,LevoParadoxIsomer,0.8,neutral,0.5
+703,2,303,1789340557,HFLevo,2.0,neutral,nan
+703,2,303,1789340557,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+704,2,304,1869217819,HFLevo,0.8,neutral,nan
+704,2,304,1869217819,LevoParadoxIsomer,0.8,neutral,0.5
+705,2,305,1673802110,HFLevo,0.8,neutral,nan
+705,2,305,1673802110,LevoParadoxIsomer,0.8,neutral,0.5
+706,2,306,62463393,HFLevo,2.0,neutral,nan
+706,2,306,62463393,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+707,2,307,2074841997,HFLevo,1.2,neutral,nan
+707,2,307,2074841997,LevoParadoxIsomer,0.0,neutral,0.1499999612569809
+708,2,308,1764116324,HFLevo,1.2,neutral,nan
+708,2,308,1764116324,LevoParadoxIsomer,0.0,neutral,0.2999999523162842
+709,2,309,1361763883,HFLevo,1.2,neutral,nan
+709,2,309,1361763883,LevoParadoxIsomer,0.0,neutral,0.2999999523162842
+710,2,310,2068149571,HFLevo,0.0,neutral,nan
+710,2,310,2068149571,LevoParadoxIsomer,0.0,neutral,0.5
+711,2,311,1761270974,HFLevo,0.8,neutral,nan
+711,2,311,1761270974,LevoParadoxIsomer,0.8,neutral,0.5
+712,2,312,1610445955,HFLevo,0.8,neutral,nan
+712,2,312,1610445955,LevoParadoxIsomer,0.0,neutral,0.5
+713,2,313,527574022,HFLevo,0.0,neutral,nan
+713,2,313,527574022,LevoParadoxIsomer,0.8,neutral,0.5
+714,2,314,1948416385,HFLevo,0.0,neutral,nan
+714,2,314,1948416385,LevoParadoxIsomer,0.0,neutral,0.5
+715,2,315,842515162,HFLevo,1.2,neutral,nan
+715,2,315,842515162,LevoParadoxIsomer,-3.0,overconfident,0.1499999612569809
+716,2,316,728127582,HFLevo,0.8,neutral,nan
+716,2,316,728127582,LevoParadoxIsomer,0.8,neutral,0.5
+717,2,317,1807809684,HFLevo,2.0,neutral,nan
+717,2,317,1807809684,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+718,2,318,1911608906,HFLevo,0.8,neutral,nan
+718,2,318,1911608906,LevoParadoxIsomer,0.0,neutral,0.5
+719,2,319,507860031,HFLevo,0.0,neutral,nan
+719,2,319,507860031,LevoParadoxIsomer,0.0,neutral,0.5
+720,2,320,1559660249,HFLevo,0.8,neutral,nan
+720,2,320,1559660249,LevoParadoxIsomer,0.8,neutral,0.5
+721,2,321,817113483,HFLevo,0.0,neutral,nan
+721,2,321,817113483,LevoParadoxIsomer,0.0,neutral,0.5
+722,2,322,181612330,HFLevo,0.8,neutral,nan
+722,2,322,181612330,LevoParadoxIsomer,0.8,neutral,0.5
+723,2,323,330146939,HFLevo,0.0,neutral,nan
+723,2,323,330146939,LevoParadoxIsomer,0.8,neutral,0.5
+724,2,324,1451519916,HFLevo,0.0,neutral,nan
+724,2,324,1451519916,LevoParadoxIsomer,0.8,neutral,0.5
+725,2,325,429715005,HFLevo,0.0,neutral,nan
+725,2,325,429715005,LevoParadoxIsomer,0.8,neutral,0.5
+726,2,326,1836744305,HFLevo,2.0,neutral,nan
+726,2,326,1836744305,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+727,2,327,200999922,HFLevo,0.8,neutral,nan
+727,2,327,200999922,LevoParadoxIsomer,0.8,neutral,0.5
+728,2,328,686960989,HFLevo,1.2,neutral,nan
+728,2,328,686960989,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+729,2,329,2137759524,HFLevo,0.0,neutral,nan
+729,2,329,2137759524,LevoParadoxIsomer,0.0,neutral,0.5
+730,2,330,48470119,HFLevo,0.0,neutral,nan
+730,2,330,48470119,LevoParadoxIsomer,0.0,neutral,0.5
+731,2,331,1429722682,HFLevo,0.0,neutral,nan
+731,2,331,1429722682,LevoParadoxIsomer,0.0,neutral,0.5
+732,2,332,218074447,HFLevo,0.0,neutral,nan
+732,2,332,218074447,LevoParadoxIsomer,0.0,neutral,0.5
+733,2,333,2104545774,HFLevo,0.0,neutral,nan
+733,2,333,2104545774,LevoParadoxIsomer,0.0,neutral,0.5
+734,2,334,1816453390,HFLevo,0.0,neutral,nan
+734,2,334,1816453390,LevoParadoxIsomer,0.0,neutral,0.5
+735,2,335,639577193,HFLevo,0.8,neutral,nan
+735,2,335,639577193,LevoParadoxIsomer,0.0,neutral,0.5
+736,2,336,231860645,HFLevo,1.2,neutral,nan
+736,2,336,231860645,LevoParadoxIsomer,1.2,neutral,0.09999996423721313
+737,2,337,1142197128,HFLevo,0.0,neutral,nan
+737,2,337,1142197128,LevoParadoxIsomer,0.0,neutral,0.5
+738,2,338,1321680746,HFLevo,0.8,neutral,nan
+738,2,338,1321680746,LevoParadoxIsomer,0.8,neutral,0.5
+739,2,339,1746554905,HFLevo,0.0,neutral,nan
+739,2,339,1746554905,LevoParadoxIsomer,0.8,neutral,0.5
+740,2,340,396707811,HFLevo,0.0,neutral,nan
+740,2,340,396707811,LevoParadoxIsomer,0.8,neutral,0.5
+741,2,341,491514850,HFLevo,0.8,neutral,nan
+741,2,341,491514850,LevoParadoxIsomer,0.8,neutral,0.5
+742,2,342,461674718,HFLevo,2.0,neutral,nan
+742,2,342,461674718,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+743,2,343,1701500925,HFLevo,0.8,neutral,nan
+743,2,343,1701500925,LevoParadoxIsomer,0.0,neutral,0.5
+744,2,344,1344298913,HFLevo,0.8,neutral,nan
+744,2,344,1344298913,LevoParadoxIsomer,0.0,neutral,0.5
+745,2,345,1354823084,HFLevo,0.8,neutral,nan
+745,2,345,1354823084,LevoParadoxIsomer,0.0,neutral,0.5
+746,2,346,118160105,HFLevo,1.2,neutral,nan
+746,2,346,118160105,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+747,2,347,1044584312,HFLevo,0.8,neutral,nan
+747,2,347,1044584312,LevoParadoxIsomer,0.0,neutral,0.5
+748,2,348,1524392230,HFLevo,1.2,neutral,nan
+748,2,348,1524392230,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+749,2,349,428869864,HFLevo,0.8,neutral,nan
+749,2,349,428869864,LevoParadoxIsomer,0.8,neutral,0.5
+750,2,350,180994417,HFLevo,0.0,neutral,nan
+750,2,350,180994417,LevoParadoxIsomer,0.0,neutral,0.5
+751,2,351,885019589,HFLevo,0.8,neutral,nan
+751,2,351,885019589,LevoParadoxIsomer,0.8,neutral,0.5
+752,2,352,1231928222,HFLevo,0.8,neutral,nan
+752,2,352,1231928222,LevoParadoxIsomer,0.8,neutral,0.5
+753,2,353,1670237802,HFLevo,0.8,neutral,nan
+753,2,353,1670237802,LevoParadoxIsomer,0.8,neutral,0.5
+754,2,354,1414482049,HFLevo,0.0,neutral,nan
+754,2,354,1414482049,LevoParadoxIsomer,0.0,neutral,0.5
+755,2,355,1971477631,HFLevo,2.0,neutral,nan
+755,2,355,1971477631,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+756,2,356,883215094,HFLevo,-3.0,overconfident,nan
+756,2,356,883215094,LevoParadoxIsomer,1.2,neutral,0.09999996423721313
+757,2,357,343285816,HFLevo,1.2,neutral,nan
+757,2,357,343285816,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+758,2,358,1993664325,HFLevo,0.0,neutral,nan
+758,2,358,1993664325,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+759,2,359,538994041,HFLevo,0.0,neutral,nan
+759,2,359,538994041,LevoParadoxIsomer,0.8,neutral,0.5
+760,2,360,365720817,HFLevo,2.0,neutral,nan
+760,2,360,365720817,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+761,2,361,1931073180,HFLevo,0.0,neutral,nan
+761,2,361,1931073180,LevoParadoxIsomer,0.8,neutral,0.5
+762,2,362,330654991,HFLevo,0.0,neutral,nan
+762,2,362,330654991,LevoParadoxIsomer,0.0,neutral,0.5
+763,2,363,2117986561,HFLevo,-3.0,overconfident,nan
+763,2,363,2117986561,LevoParadoxIsomer,1.2,neutral,0.1499999612569809
+764,2,364,1745610389,HFLevo,2.0,neutral,nan
+764,2,364,1745610389,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+765,2,365,1935491097,HFLevo,0.8,neutral,nan
+765,2,365,1935491097,LevoParadoxIsomer,0.0,neutral,0.5
+766,2,366,816460352,HFLevo,0.8,neutral,nan
+766,2,366,816460352,LevoParadoxIsomer,0.8,neutral,0.5
+767,2,367,2034287239,HFLevo,0.0,neutral,nan
+767,2,367,2034287239,LevoParadoxIsomer,0.8,neutral,0.5
+768,2,368,1494357375,HFLevo,0.8,neutral,nan
+768,2,368,1494357375,LevoParadoxIsomer,0.0,neutral,0.5
+769,2,369,525099930,HFLevo,0.8,neutral,nan
+769,2,369,525099930,LevoParadoxIsomer,0.0,neutral,0.5
+770,2,370,461861420,HFLevo,0.8,neutral,nan
+770,2,370,461861420,LevoParadoxIsomer,0.8,neutral,0.5
+771,2,371,424434369,HFLevo,0.0,neutral,nan
+771,2,371,424434369,LevoParadoxIsomer,0.0,neutral,0.5
+772,2,372,1696040440,HFLevo,2.0,neutral,nan
+772,2,372,1696040440,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+773,2,373,1327732050,HFLevo,0.0,neutral,nan
+773,2,373,1327732050,LevoParadoxIsomer,0.0,neutral,0.5
+774,2,374,1275639253,HFLevo,0.8,neutral,nan
+774,2,374,1275639253,LevoParadoxIsomer,0.8,neutral,0.5
+775,2,375,180175624,HFLevo,0.8,neutral,nan
+775,2,375,180175624,LevoParadoxIsomer,0.0,neutral,0.5
+776,2,376,853325992,HFLevo,0.0,neutral,nan
+776,2,376,853325992,LevoParadoxIsomer,1.2,neutral,0.1499999612569809
+777,2,377,35314844,HFLevo,0.8,neutral,nan
+777,2,377,35314844,LevoParadoxIsomer,0.0,neutral,0.5
+778,2,378,512421119,HFLevo,2.0,neutral,nan
+778,2,378,512421119,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+779,2,379,1818877815,HFLevo,0.8,neutral,nan
+779,2,379,1818877815,LevoParadoxIsomer,0.8,neutral,0.5
+780,2,380,244612068,HFLevo,0.8,neutral,nan
+780,2,380,244612068,LevoParadoxIsomer,0.8,neutral,0.5
+781,2,381,438090890,HFLevo,0.0,neutral,nan
+781,2,381,438090890,LevoParadoxIsomer,0.8,neutral,0.5
+782,2,382,71908212,HFLevo,0.8,neutral,nan
+782,2,382,71908212,LevoParadoxIsomer,0.8,neutral,0.5
+783,2,383,763979621,HFLevo,0.8,neutral,nan
+783,2,383,763979621,LevoParadoxIsomer,0.0,neutral,0.5
+784,2,384,492409950,HFLevo,0.8,neutral,nan
+784,2,384,492409950,LevoParadoxIsomer,0.8,neutral,0.5
+785,2,385,1939330706,HFLevo,-3.0,overconfident,nan
+785,2,385,1939330706,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+786,2,386,304266562,HFLevo,0.8,neutral,nan
+786,2,386,304266562,LevoParadoxIsomer,0.8,neutral,0.5
+787,2,387,1839152413,HFLevo,0.0,neutral,nan
+787,2,387,1839152413,LevoParadoxIsomer,0.8,neutral,0.5
+788,2,388,1584964798,HFLevo,0.8,neutral,nan
+788,2,388,1584964798,LevoParadoxIsomer,0.0,neutral,0.5
+789,2,389,1705345529,HFLevo,0.0,neutral,nan
+789,2,389,1705345529,LevoParadoxIsomer,0.0,neutral,0.5
+790,2,390,530680927,HFLevo,0.0,neutral,nan
+790,2,390,530680927,LevoParadoxIsomer,-0.8,overcautious,0.8000000715255737
+791,2,391,601158382,HFLevo,1.2,neutral,nan
+791,2,391,601158382,LevoParadoxIsomer,-3.0,overconfident,0.09999996423721313
+792,2,392,661312537,HFLevo,0.8,neutral,nan
+792,2,392,661312537,LevoParadoxIsomer,0.0,neutral,0.5
+793,2,393,1099910893,HFLevo,0.0,neutral,nan
+793,2,393,1099910893,LevoParadoxIsomer,0.8,neutral,0.5
+794,2,394,1777836792,HFLevo,-3.0,overconfident,nan
+794,2,394,1777836792,LevoParadoxIsomer,-3.0,overconfident,0.049999963492155075
+795,2,395,320819501,HFLevo,0.0,neutral,nan
+795,2,395,320819501,LevoParadoxIsomer,0.8,neutral,0.5
+796,2,396,1262930064,HFLevo,0.8,neutral,nan
+796,2,396,1262930064,LevoParadoxIsomer,0.8,neutral,0.5
+797,2,397,1476382876,HFLevo,0.0,neutral,nan
+797,2,397,1476382876,LevoParadoxIsomer,0.0,neutral,0.5
+798,2,398,229480971,HFLevo,0.8,neutral,nan
+798,2,398,229480971,LevoParadoxIsomer,0.8,neutral,0.5
+799,2,399,1346765522,HFLevo,0.8,neutral,nan
+799,2,399,1346765522,LevoParadoxIsomer,0.8,neutral,0.5
+800,3,0,1015467875,HFLevo,1.2902930843845795,neutral,nan
+800,3,0,1015467875,LevoParadoxIsomer,0.49029308438457947,neutral,0.5
+801,3,1,871703027,HFLevo,0.2583186351063763,neutral,nan
+801,3,1,871703027,LevoParadoxIsomer,0.2583186351063763,neutral,0.5
+802,3,2,1121193219,HFLevo,-0.9285687801394484,overcautious,nan
+802,3,2,1121193219,LevoParadoxIsomer,-0.9285687801394484,overcautious,0.5
+803,3,3,1844331605,HFLevo,1.268712954493355,neutral,nan
+803,3,3,1844331605,LevoParadoxIsomer,2.068712954493355,neutral,0.5
+804,3,4,376821421,HFLevo,-2.1540989698282287,overconfident,nan
+804,3,4,376821421,LevoParadoxIsomer,1.0459010301717713,neutral,0.5
+805,3,5,1528947293,HFLevo,1.9236203197456223,neutral,nan
+805,3,5,1528947293,LevoParadoxIsomer,0.7236203197456224,neutral,0.5
+806,3,6,1457385050,HFLevo,-0.388878696540991,neutral,nan
+806,3,6,1457385050,LevoParadoxIsomer,-0.388878696540991,neutral,0.5
+807,3,7,1930833205,HFLevo,1.8947708652049988,neutral,nan
+807,3,7,1930833205,LevoParadoxIsomer,1.8947708652049988,neutral,0.5
+808,3,8,331658631,HFLevo,-0.34637226145235095,overcautious,nan
+808,3,8,331658631,LevoParadoxIsomer,2.4536277385476493,neutral,0.5
+809,3,9,1392760183,HFLevo,-1.7470900488231007,overconfident,nan
+809,3,9,1392760183,LevoParadoxIsomer,-1.7470900488231007,overconfident,0.5
+810,3,10,1410154195,HFLevo,-0.424961650515833,neutral,nan
+810,3,10,1410154195,LevoParadoxIsomer,0.37503834948416703,neutral,0.5
+811,3,11,1330897656,HFLevo,0.7046013636486836,neutral,nan
+811,3,11,1330897656,LevoParadoxIsomer,-1.2953986363513164,overconfident,0.5
+812,3,12,755426389,HFLevo,0.7155068417757497,neutral,nan
+812,3,12,755426389,LevoParadoxIsomer,0.7155068417757497,neutral,0.5
+813,3,13,478344337,HFLevo,0.5966519575139582,neutral,nan
+813,3,13,478344337,LevoParadoxIsomer,0.5966519575139582,neutral,0.5
+814,3,14,2135473267,HFLevo,-0.321538017298501,neutral,nan
+814,3,14,2135473267,LevoParadoxIsomer,-0.321538017298501,neutral,0.5
+815,3,15,1381920965,HFLevo,-0.8946149359899849,neutral,nan
+815,3,15,1381920965,LevoParadoxIsomer,-0.8946149359899849,neutral,0.5
+816,3,16,790353978,HFLevo,0.005089675891585976,neutral,nan
+816,3,16,790353978,LevoParadoxIsomer,0.005089675891585976,neutral,0.5
+817,3,17,886917887,HFLevo,1.1565439534575777,neutral,nan
+817,3,17,886917887,LevoParadoxIsomer,1.1565439534575777,neutral,0.44999998807907104
+818,3,18,1326287162,HFLevo,1.1505608969734533,neutral,nan
+818,3,18,1326287162,LevoParadoxIsomer,1.1505608969734533,neutral,0.5
+819,3,19,132467973,HFLevo,0.7640856467236494,neutral,nan
+819,3,19,132467973,LevoParadoxIsomer,-2.4359143532763508,overconfident,0.44999998807907104
+820,3,20,896694920,HFLevo,-0.16856310825832727,neutral,nan
+820,3,20,896694920,LevoParadoxIsomer,0.6314368917416728,neutral,0.5
+821,3,21,265295982,HFLevo,0.472286750236512,neutral,nan
+821,3,21,265295982,LevoParadoxIsomer,-0.32771324976348803,neutral,0.5
+822,3,22,188123431,HFLevo,0.7270211373649718,neutral,nan
+822,3,22,188123431,LevoParadoxIsomer,0.7270211373649718,neutral,0.5
+823,3,23,1385912200,HFLevo,-0.2257286159489377,neutral,nan
+823,3,23,1385912200,LevoParadoxIsomer,-0.2257286159489377,neutral,0.5
+824,3,24,449987729,HFLevo,-0.6492071611207221,overcautious,nan
+824,3,24,449987729,LevoParadoxIsomer,-0.6492071611207221,overcautious,0.5
+825,3,25,593302824,HFLevo,1.1549840686447408,neutral,nan
+825,3,25,593302824,LevoParadoxIsomer,-0.045015931355259255,neutral,0.44999998807907104
+826,3,26,194879931,HFLevo,0.17815411284413574,neutral,nan
+826,3,26,194879931,LevoParadoxIsomer,0.9781541128441358,neutral,0.5
+827,3,27,1758518115,HFLevo,0.24558567387383792,neutral,nan
+827,3,27,1758518115,LevoParadoxIsomer,0.24558567387383792,neutral,0.5
+828,3,28,1545311517,HFLevo,0.1627856125489353,neutral,nan
+828,3,28,1545311517,LevoParadoxIsomer,0.1627856125489353,neutral,0.5
+829,3,29,562603550,HFLevo,0.4183364147364109,neutral,nan
+829,3,29,562603550,LevoParadoxIsomer,1.2183364147364109,neutral,0.5
+830,3,30,1059808449,HFLevo,1.2917831804345576,neutral,nan
+830,3,30,1059808449,LevoParadoxIsomer,1.2917831804345576,neutral,0.5
+831,3,31,614383066,HFLevo,0.8163422218854106,neutral,nan
+831,3,31,614383066,LevoParadoxIsomer,0.016342221885410586,neutral,0.5
+832,3,32,1192512057,HFLevo,0.25576402304424295,neutral,nan
+832,3,32,1192512057,LevoParadoxIsomer,0.25576402304424295,neutral,0.5
+833,3,33,950581637,HFLevo,-0.8977958223217277,neutral,nan
+833,3,33,950581637,LevoParadoxIsomer,-0.09779582232172768,neutral,0.5
+834,3,34,1863772308,HFLevo,1.253089981409791,neutral,nan
+834,3,34,1863772308,LevoParadoxIsomer,0.45308998140979106,neutral,0.5
+835,3,35,1007304414,HFLevo,0.5254077508874286,neutral,nan
+835,3,35,1007304414,LevoParadoxIsomer,0.5254077508874286,neutral,0.5
+836,3,36,809066309,HFLevo,1.688901367855694,neutral,nan
+836,3,36,809066309,LevoParadoxIsomer,1.688901367855694,neutral,0.550000011920929
+837,3,37,1514006645,HFLevo,0.8581530848584542,neutral,nan
+837,3,37,1514006645,LevoParadoxIsomer,0.8581530848584542,neutral,0.44999998807907104
+838,3,38,1146495174,HFLevo,-1.0942163006184566,neutral,nan
+838,3,38,1146495174,LevoParadoxIsomer,-1.0942163006184566,neutral,0.5
+839,3,39,1859928275,HFLevo,2.5861061474664324,neutral,nan
+839,3,39,1859928275,LevoParadoxIsomer,-0.21389385253356752,overcautious,0.5
+840,3,40,303035184,HFLevo,-0.5964859730416497,overcautious,nan
+840,3,40,303035184,LevoParadoxIsomer,0.2035140269583503,neutral,0.550000011920929
+841,3,41,1724145893,HFLevo,0.19388739728817525,neutral,nan
+841,3,41,1724145893,LevoParadoxIsomer,0.19388739728817525,neutral,0.5
+842,3,42,1035035036,HFLevo,2.053749158155912,neutral,nan
+842,3,42,1035035036,LevoParadoxIsomer,-0.746250841844088,overcautious,0.550000011920929
+843,3,43,278635683,HFLevo,-0.4576999329201501,neutral,nan
+843,3,43,278635683,LevoParadoxIsomer,-1.2576999329201501,overcautious,0.550000011920929
+844,3,44,377801781,HFLevo,0.26696367777414026,neutral,nan
+844,3,44,377801781,LevoParadoxIsomer,-0.5330363222258598,overcautious,0.550000011920929
+845,3,45,1487506785,HFLevo,-0.22698718726653985,neutral,nan
+845,3,45,1487506785,LevoParadoxIsomer,0.5730128127334602,neutral,0.5
+846,3,46,766738768,HFLevo,1.7451958636224911,neutral,nan
+846,3,46,766738768,LevoParadoxIsomer,0.5451958636224912,neutral,0.44999998807907104
+847,3,47,1469590444,HFLevo,-0.11980525970062122,neutral,nan
+847,3,47,1469590444,LevoParadoxIsomer,0.6801947402993789,neutral,0.5
+848,3,48,1527511243,HFLevo,-1.5257325358366638,overconfident,nan
+848,3,48,1527511243,LevoParadoxIsomer,-1.5257325358366638,overconfident,0.5
+849,3,49,1889868992,HFLevo,0.18740335053313484,neutral,nan
+849,3,49,1889868992,LevoParadoxIsomer,-0.6125966494668652,neutral,0.5
+850,3,50,808836466,HFLevo,1.4424045317149554,neutral,nan
+850,3,50,808836466,LevoParadoxIsomer,0.6424045317149553,neutral,0.5
+851,3,51,880209119,HFLevo,-0.41698242562591825,neutral,nan
+851,3,51,880209119,LevoParadoxIsomer,0.3830175743740818,neutral,0.5
+852,3,52,2069774123,HFLevo,1.1358277140176187,neutral,nan
+852,3,52,2069774123,LevoParadoxIsomer,1.1358277140176187,neutral,0.5
+853,3,53,59978537,HFLevo,0.4273143926451667,neutral,nan
+853,3,53,59978537,LevoParadoxIsomer,1.2273143926451668,neutral,0.5
+854,3,54,268314216,HFLevo,-0.09615231078263872,neutral,nan
+854,3,54,268314216,LevoParadoxIsomer,-2.0961523107826388,overconfident,0.3999999761581421
+855,3,55,786773386,HFLevo,1.2293653945237177,neutral,nan
+855,3,55,786773386,LevoParadoxIsomer,0.42936539452371775,neutral,0.5
+856,3,56,1078804137,HFLevo,-0.0031176348126684796,neutral,nan
+856,3,56,1078804137,LevoParadoxIsomer,1.9968823651873315,neutral,0.6000000238418579
+857,3,57,88529032,HFLevo,0.9139455244229703,neutral,nan
+857,3,57,88529032,LevoParadoxIsomer,0.9139455244229703,neutral,0.5
+858,3,58,733437594,HFLevo,0.1998437802015791,neutral,nan
+858,3,58,733437594,LevoParadoxIsomer,0.9998437802015792,neutral,0.5
+859,3,59,1895500654,HFLevo,0.3147969815448564,neutral,nan
+859,3,59,1895500654,LevoParadoxIsomer,1.1147969815448564,neutral,0.5
+860,3,60,1156794677,HFLevo,0.2822839034147425,neutral,nan
+860,3,60,1156794677,LevoParadoxIsomer,-0.5177160965852575,neutral,0.5
+861,3,61,1733698348,HFLevo,0.30837871111489873,neutral,nan
+861,3,61,1733698348,LevoParadoxIsomer,0.30837871111489873,neutral,0.5
+862,3,62,1987643195,HFLevo,0.8494336690825907,neutral,nan
+862,3,62,1987643195,LevoParadoxIsomer,0.8494336690825907,neutral,0.5
+863,3,63,968829821,HFLevo,1.4361346524157803,neutral,nan
+863,3,63,968829821,LevoParadoxIsomer,-1.3638653475842197,overcautious,0.6000000238418579
+864,3,64,2125839782,HFLevo,0.5216533205919234,neutral,nan
+864,3,64,2125839782,LevoParadoxIsomer,-0.2783466794080766,neutral,0.5
+865,3,65,1588322590,HFLevo,1.4909286951175467,neutral,nan
+865,3,65,1588322590,LevoParadoxIsomer,1.4909286951175467,neutral,0.5
+866,3,66,956367808,HFLevo,-0.13769164780464407,neutral,nan
+866,3,66,956367808,LevoParadoxIsomer,1.0623083521953558,neutral,0.34999996423721313
+867,3,67,2135886715,HFLevo,0.10203387100943626,neutral,nan
+867,3,67,2135886715,LevoParadoxIsomer,0.10203387100943626,neutral,0.5
+868,3,68,705297723,HFLevo,2.267886356355249,neutral,nan
+868,3,68,705297723,LevoParadoxIsomer,-0.5321136436447511,overcautious,0.6000000238418579
+869,3,69,493712258,HFLevo,-0.7200477872906951,neutral,nan
+869,3,69,493712258,LevoParadoxIsomer,-0.7200477872906951,neutral,0.5
+870,3,70,75420035,HFLevo,0.4007933704297794,neutral,nan
+870,3,70,75420035,LevoParadoxIsomer,0.4007933704297794,neutral,0.5
+871,3,71,1867815399,HFLevo,-1.475944225695721,overconfident,nan
+871,3,71,1867815399,LevoParadoxIsomer,1.724055774304279,neutral,0.44999998807907104
+872,3,72,509860178,HFLevo,-1.0158303213136595,neutral,nan
+872,3,72,509860178,LevoParadoxIsomer,-1.0158303213136595,neutral,0.5
+873,3,73,687658415,HFLevo,0.7555449868898535,neutral,nan
+873,3,73,687658415,LevoParadoxIsomer,-0.044455013110146525,neutral,0.5
+874,3,74,319346835,HFLevo,1.1336872360109915,neutral,nan
+874,3,74,319346835,LevoParadoxIsomer,0.33368723601099154,neutral,0.5
+875,3,75,985914893,HFLevo,2.047902947899302,neutral,nan
+875,3,75,985914893,LevoParadoxIsomer,0.8479029478993018,neutral,0.34999996423721313
+876,3,76,456914363,HFLevo,-0.5563823345326576,neutral,nan
+876,3,76,456914363,LevoParadoxIsomer,-0.5563823345326576,neutral,0.5
+877,3,77,1903234235,HFLevo,-0.6168972120738625,neutral,nan
+877,3,77,1903234235,LevoParadoxIsomer,-0.6168972120738625,neutral,0.5
+878,3,78,1104964239,HFLevo,-0.5537494140426967,neutral,nan
+878,3,78,1104964239,LevoParadoxIsomer,-0.5537494140426967,neutral,0.5
+879,3,79,800272976,HFLevo,1.0014376600959138,neutral,nan
+879,3,79,800272976,LevoParadoxIsomer,-0.19856233990408617,neutral,0.44999998807907104
+880,3,80,514077870,HFLevo,0.548889466402683,neutral,nan
+880,3,80,514077870,LevoParadoxIsomer,0.548889466402683,neutral,0.5
+881,3,81,2084198909,HFLevo,0.37575413134291114,neutral,nan
+881,3,81,2084198909,LevoParadoxIsomer,0.37575413134291114,neutral,0.5
+882,3,82,909365514,HFLevo,0.8573741369785153,neutral,nan
+882,3,82,909365514,LevoParadoxIsomer,1.6573741369785153,neutral,0.5
+883,3,83,1338386269,HFLevo,-2.0258053677846037,overconfident,nan
+883,3,83,1338386269,LevoParadoxIsomer,1.174194632215396,neutral,0.44999998807907104
+884,3,84,947429886,HFLevo,0.6408681653569512,neutral,nan
+884,3,84,947429886,LevoParadoxIsomer,0.6408681653569512,neutral,0.5
+885,3,85,58590834,HFLevo,0.524978671615042,neutral,nan
+885,3,85,58590834,LevoParadoxIsomer,1.324978671615042,neutral,0.5
+886,3,86,684669113,HFLevo,-0.03636158399340117,neutral,nan
+886,3,86,684669113,LevoParadoxIsomer,0.7636384160065989,neutral,0.5
+887,3,87,898917724,HFLevo,-1.2982161592441008,overcautious,nan
+887,3,87,898917724,LevoParadoxIsomer,-0.49821615924410073,neutral,0.6500000357627869
+888,3,88,548630351,HFLevo,0.5729339230581685,neutral,nan
+888,3,88,548630351,LevoParadoxIsomer,-0.2270660769418316,neutral,0.5
+889,3,89,1616758964,HFLevo,1.9765640849812711,neutral,nan
+889,3,89,1616758964,LevoParadoxIsomer,0.776564084981271,neutral,0.34999996423721313
+890,3,90,2082505679,HFLevo,0.10646213282367678,neutral,nan
+890,3,90,2082505679,LevoParadoxIsomer,-0.6935378671763233,neutral,0.5
+891,3,91,741581883,HFLevo,-0.0517050103645994,neutral,nan
+891,3,91,741581883,LevoParadoxIsomer,-0.8517050103645994,neutral,0.5
+892,3,92,393553070,HFLevo,-1.66724885658832,overconfident,nan
+892,3,92,393553070,LevoParadoxIsomer,-1.66724885658832,overconfident,0.44999998807907104
+893,3,93,890404536,HFLevo,0.11880035600180827,neutral,nan
+893,3,93,890404536,LevoParadoxIsomer,0.11880035600180827,neutral,0.5
+894,3,94,179225205,HFLevo,1.1151171438000786,neutral,nan
+894,3,94,179225205,LevoParadoxIsomer,1.1151171438000786,neutral,0.6500000357627869
+895,3,95,852483643,HFLevo,0.2428840098478961,neutral,nan
+895,3,95,852483643,LevoParadoxIsomer,1.0428840098478962,neutral,0.5
+896,3,96,596549345,HFLevo,0.24399029764900915,neutral,nan
+896,3,96,596549345,LevoParadoxIsomer,1.0439902976490092,neutral,0.5
+897,3,97,1246421089,HFLevo,1.363801434612391,neutral,nan
+897,3,97,1246421089,LevoParadoxIsomer,1.363801434612391,neutral,0.3999999761581421
+898,3,98,505921964,HFLevo,1.341472357060377,neutral,nan
+898,3,98,505921964,LevoParadoxIsomer,1.341472357060377,neutral,0.34999996423721313
+899,3,99,1670483738,HFLevo,0.36815436045097727,neutral,nan
+899,3,99,1670483738,LevoParadoxIsomer,2.368154360450977,neutral,0.6500000357627869
+900,3,100,598294997,HFLevo,0.5186734793084872,neutral,nan
+900,3,100,598294997,LevoParadoxIsomer,0.5186734793084872,neutral,0.5
+901,3,101,1370406670,HFLevo,-0.6009999531995625,overcautious,nan
+901,3,101,1370406670,LevoParadoxIsomer,2.1990000468004376,neutral,0.6500000357627869
+902,3,102,1690182943,HFLevo,-0.7096418067537537,neutral,nan
+902,3,102,1690182943,LevoParadoxIsomer,-0.7096418067537537,neutral,0.44999998807907104
+903,3,103,1763960416,HFLevo,0.9006844579184998,neutral,nan
+903,3,103,1763960416,LevoParadoxIsomer,0.9006844579184998,neutral,0.5
+904,3,104,1645746969,HFLevo,-1.5486128539981796,overconfident,nan
+904,3,104,1645746969,LevoParadoxIsomer,1.6513871460018201,neutral,0.44999998807907104
+905,3,105,820931753,HFLevo,0.962884542235,neutral,nan
+905,3,105,820931753,LevoParadoxIsomer,0.962884542235,neutral,0.5
+906,3,106,1937660724,HFLevo,-1.7950840687430392,overconfident,nan
+906,3,106,1937660724,LevoParadoxIsomer,1.4049159312569608,neutral,0.3999999761581421
+907,3,107,536112434,HFLevo,-0.6515974640687232,neutral,nan
+907,3,107,536112434,LevoParadoxIsomer,0.14840253593127684,neutral,0.5
+908,3,108,852564596,HFLevo,-1.217424430883582,overconfident,nan
+908,3,108,852564596,LevoParadoxIsomer,-1.217424430883582,overconfident,0.34999996423721313
+909,3,109,2109579034,HFLevo,0.04948327477624494,neutral,nan
+909,3,109,2109579034,LevoParadoxIsomer,0.849483274776245,neutral,0.5
+910,3,110,1119218651,HFLevo,-0.43896650430096007,overcautious,nan
+910,3,110,1119218651,LevoParadoxIsomer,-0.43896650430096007,overcautious,0.6500000357627869
+911,3,111,1171844126,HFLevo,2.62092499583073,neutral,nan
+911,3,111,1171844126,LevoParadoxIsomer,2.62092499583073,neutral,0.6500000357627869
+912,3,112,1066832,HFLevo,0.40745352051750494,neutral,nan
+912,3,112,1066832,LevoParadoxIsomer,1.607453520517505,neutral,0.3999999761581421
+913,3,113,2039855300,HFLevo,1.695118384619502,neutral,nan
+913,3,113,2039855300,LevoParadoxIsomer,0.495118384619502,neutral,0.2999999523162842
+914,3,114,1830413706,HFLevo,0.6645794180980706,neutral,nan
+914,3,114,1830413706,LevoParadoxIsomer,0.6645794180980706,neutral,0.5
+915,3,115,1536434038,HFLevo,-2.4062797861421963,overconfident,nan
+915,3,115,1536434038,LevoParadoxIsomer,0.7937202138578034,neutral,0.3999999761581421
+916,3,116,204181968,HFLevo,0.6050677080581792,neutral,nan
+916,3,116,204181968,LevoParadoxIsomer,-0.19493229194182082,neutral,0.5
+917,3,117,782074719,HFLevo,0.5091854411977584,neutral,nan
+917,3,117,782074719,LevoParadoxIsomer,1.3091854411977586,neutral,0.5
+918,3,118,1683677695,HFLevo,-2.133018434655278,overconfident,nan
+918,3,118,1683677695,LevoParadoxIsomer,1.0669815653447223,neutral,0.44999998807907104
+919,3,119,811003462,HFLevo,1.0318839391215868,neutral,nan
+919,3,119,811003462,LevoParadoxIsomer,0.23188393912158678,neutral,0.5
+920,3,120,1843531798,HFLevo,1.0588472647494347,neutral,nan
+920,3,120,1843531798,LevoParadoxIsomer,-2.1411527352505653,overconfident,0.44999998807907104
+921,3,121,244956048,HFLevo,0.13156308418899432,neutral,nan
+921,3,121,244956048,LevoParadoxIsomer,-1.0684369158110056,neutral,0.3999999761581421
+922,3,122,521744775,HFLevo,0.47193103591872976,neutral,nan
+922,3,122,521744775,LevoParadoxIsomer,1.2719310359187297,neutral,0.5
+923,3,123,137476059,HFLevo,0.7847373937495904,neutral,nan
+923,3,123,137476059,LevoParadoxIsomer,2.7847373937495905,neutral,0.6500000357627869
+924,3,124,1493249395,HFLevo,0.7601033690157201,neutral,nan
+924,3,124,1493249395,LevoParadoxIsomer,0.7601033690157201,neutral,0.5
+925,3,125,1658377656,HFLevo,0.794286174152496,neutral,nan
+925,3,125,1658377656,LevoParadoxIsomer,-0.0057138258475040145,neutral,0.5
+926,3,126,1598149764,HFLevo,-0.7555320294233012,overcautious,nan
+926,3,126,1598149764,LevoParadoxIsomer,-0.7555320294233012,overcautious,0.6500000357627869
+927,3,127,1973871594,HFLevo,-0.9718334196844586,neutral,nan
+927,3,127,1973871594,LevoParadoxIsomer,-0.17183341968445853,neutral,0.5
+928,3,128,1575988774,HFLevo,1.1486204800254285,neutral,nan
+928,3,128,1575988774,LevoParadoxIsomer,0.34862048002542856,neutral,0.5
+929,3,129,352160276,HFLevo,2.704477659368699,neutral,nan
+929,3,129,352160276,LevoParadoxIsomer,0.7044776593686989,neutral,0.7000000476837158
+930,3,130,1951098323,HFLevo,2.4905644877611,neutral,nan
+930,3,130,1951098323,LevoParadoxIsomer,2.4905644877611,neutral,0.7000000476837158
+931,3,131,496971788,HFLevo,0.12521537161674298,neutral,nan
+931,3,131,496971788,LevoParadoxIsomer,0.12521537161674298,neutral,0.5
+932,3,132,1527624590,HFLevo,-0.5214567208411459,neutral,nan
+932,3,132,1527624590,LevoParadoxIsomer,0.27854327915885413,neutral,0.5
+933,3,133,400244675,HFLevo,0.4690883568339111,neutral,nan
+933,3,133,400244675,LevoParadoxIsomer,1.269088356833911,neutral,0.5
+934,3,134,386029926,HFLevo,1.6115419185654172,neutral,nan
+934,3,134,386029926,LevoParadoxIsomer,1.6115419185654172,neutral,0.5
+935,3,135,573115058,HFLevo,-0.46511297476372343,neutral,nan
+935,3,135,573115058,LevoParadoxIsomer,-0.46511297476372343,neutral,0.5
+936,3,136,575530726,HFLevo,-0.4869844334164761,neutral,nan
+936,3,136,575530726,LevoParadoxIsomer,-1.2869844334164762,neutral,0.5
+937,3,137,1964806113,HFLevo,0.8466030235631011,neutral,nan
+937,3,137,1964806113,LevoParadoxIsomer,0.8466030235631011,neutral,0.5
+938,3,138,1773149752,HFLevo,1.3243703157591027,neutral,nan
+938,3,138,1773149752,LevoParadoxIsomer,-0.6756296842408973,neutral,0.6000000238418579
+939,3,139,1241865463,HFLevo,-0.04740295264952519,neutral,nan
+939,3,139,1241865463,LevoParadoxIsomer,0.7525970473504748,neutral,0.5
+940,3,140,330338515,HFLevo,1.344359524627644,neutral,nan
+940,3,140,330338515,LevoParadoxIsomer,1.344359524627644,neutral,0.2999999523162842
+941,3,141,761423743,HFLevo,1.498481325424641,neutral,nan
+941,3,141,761423743,LevoParadoxIsomer,1.498481325424641,neutral,0.7000000476837158
+942,3,142,455706626,HFLevo,0.34741717796209975,neutral,nan
+942,3,142,455706626,LevoParadoxIsomer,-0.4525828220379003,neutral,0.5
+943,3,143,1509528211,HFLevo,0.26559561736173787,neutral,nan
+943,3,143,1509528211,LevoParadoxIsomer,0.26559561736173787,neutral,0.5
+944,3,144,1356675324,HFLevo,0.16250138667109204,neutral,nan
+944,3,144,1356675324,LevoParadoxIsomer,0.16250138667109204,neutral,0.3999999761581421
+945,3,145,567724229,HFLevo,0.029562369865580456,neutral,nan
+945,3,145,567724229,LevoParadoxIsomer,1.2295623698655804,neutral,0.3999999761581421
+946,3,146,490459625,HFLevo,-0.4252469944816251,neutral,nan
+946,3,146,490459625,LevoParadoxIsomer,0.3747530055183749,neutral,0.5
+947,3,147,1407038082,HFLevo,-0.4074473579795859,neutral,nan
+947,3,147,1407038082,LevoParadoxIsomer,-0.4074473579795859,neutral,0.5
+948,3,148,247701853,HFLevo,0.8320849787931718,neutral,nan
+948,3,148,247701853,LevoParadoxIsomer,1.6320849787931717,neutral,0.5
+949,3,149,1656318998,HFLevo,1.3550404894059949,neutral,nan
+949,3,149,1656318998,LevoParadoxIsomer,-1.844959510594005,overconfident,0.2999999523162842
+950,3,150,1050418889,HFLevo,1.487598882153529,neutral,nan
+950,3,150,1050418889,LevoParadoxIsomer,0.287598882153529,neutral,0.3999999761581421
+951,3,151,1647828121,HFLevo,-2.585520438420843,overconfident,nan
+951,3,151,1647828121,LevoParadoxIsomer,0.6144795615791568,neutral,0.24999995529651642
+952,3,152,1665251223,HFLevo,-0.34010762403862854,neutral,nan
+952,3,152,1665251223,LevoParadoxIsomer,0.4598923759613715,neutral,0.5
+953,3,153,1158119200,HFLevo,-2.301078011092654,overconfident,nan
+953,3,153,1158119200,LevoParadoxIsomer,0.8989219889073461,neutral,0.3999999761581421
+954,3,154,1745086003,HFLevo,0.1934767393490589,neutral,nan
+954,3,154,1745086003,LevoParadoxIsomer,0.1934767393490589,neutral,0.24999995529651642
+955,3,155,1329952935,HFLevo,0.4028712963253857,neutral,nan
+955,3,155,1329952935,LevoParadoxIsomer,0.4028712963253857,neutral,0.5
+956,3,156,129216577,HFLevo,0.8226525683891055,neutral,nan
+956,3,156,129216577,LevoParadoxIsomer,0.8226525683891055,neutral,0.5
+957,3,157,264447624,HFLevo,-0.2925163838635117,neutral,nan
+957,3,157,264447624,LevoParadoxIsomer,0.5074836161364884,neutral,0.5
+958,3,158,1239906804,HFLevo,-0.3826083405585227,neutral,nan
+958,3,158,1239906804,LevoParadoxIsomer,0.4173916594414773,neutral,0.5
+959,3,159,1968251934,HFLevo,0.13882640780924027,neutral,nan
+959,3,159,1968251934,LevoParadoxIsomer,0.13882640780924027,neutral,0.3999999761581421
+960,3,160,1710361518,HFLevo,0.24410679553415088,neutral,nan
+960,3,160,1710361518,LevoParadoxIsomer,0.24410679553415088,neutral,0.5
+961,3,161,1919087800,HFLevo,1.3080289495019592,neutral,nan
+961,3,161,1919087800,LevoParadoxIsomer,0.5080289495019592,neutral,0.5
+962,3,162,1038210496,HFLevo,-0.795060643378411,neutral,nan
+962,3,162,1038210496,LevoParadoxIsomer,-0.795060643378411,neutral,0.5
+963,3,163,1786593305,HFLevo,-0.4133444185482581,neutral,nan
+963,3,163,1786593305,LevoParadoxIsomer,-0.4133444185482581,neutral,0.5
+964,3,164,2098346951,HFLevo,0.37264748440059975,neutral,nan
+964,3,164,2098346951,LevoParadoxIsomer,1.1726474844005998,neutral,0.5
+965,3,165,1526904777,HFLevo,0.24330040162795474,neutral,nan
+965,3,165,1526904777,LevoParadoxIsomer,-0.5566995983720453,overcautious,0.6000000238418579
+966,3,166,1540522504,HFLevo,-0.48669767260032276,neutral,nan
+966,3,166,1540522504,LevoParadoxIsomer,-0.48669767260032276,neutral,0.5
+967,3,167,2109348201,HFLevo,0.6798652475070099,neutral,nan
+967,3,167,2109348201,LevoParadoxIsomer,0.6798652475070099,neutral,0.5
+968,3,168,1132622124,HFLevo,2.1098705387834524,neutral,nan
+968,3,168,1132622124,LevoParadoxIsomer,2.1098705387834524,neutral,0.7000000476837158
+969,3,169,1615465936,HFLevo,0.8935436568965417,neutral,nan
+969,3,169,1615465936,LevoParadoxIsomer,-0.30645634310345826,neutral,0.24999995529651642
+970,3,170,364326948,HFLevo,0.2815486952393436,neutral,nan
+970,3,170,364326948,LevoParadoxIsomer,1.0815486952393436,neutral,0.5
+971,3,171,1220814450,HFLevo,1.6723429375012457,neutral,nan
+971,3,171,1220814450,LevoParadoxIsomer,1.6723429375012457,neutral,0.5
+972,3,172,731873456,HFLevo,0.612363357663838,neutral,nan
+972,3,172,731873456,LevoParadoxIsomer,0.612363357663838,neutral,0.5
+973,3,173,1247536227,HFLevo,-0.007119122154074176,neutral,nan
+973,3,173,1247536227,LevoParadoxIsomer,-0.8071191221540742,neutral,0.5
+974,3,174,1337517349,HFLevo,0.7525185820761515,neutral,nan
+974,3,174,1337517349,LevoParadoxIsomer,-0.04748141792384848,neutral,0.5
+975,3,175,988735470,HFLevo,-1.0294181839139849,neutral,nan
+975,3,175,988735470,LevoParadoxIsomer,-0.22941818391398483,neutral,0.5
+976,3,176,624782456,HFLevo,1.008964131480814,neutral,nan
+976,3,176,624782456,LevoParadoxIsomer,1.008964131480814,neutral,0.5
+977,3,177,1614653143,HFLevo,-0.4293029498830948,neutral,nan
+977,3,177,1614653143,LevoParadoxIsomer,0.37069705011690524,neutral,0.5
+978,3,178,1500038813,HFLevo,1.1161432423615547,neutral,nan
+978,3,178,1500038813,LevoParadoxIsomer,1.9161432423615548,neutral,0.5
+979,3,179,2130149343,HFLevo,0.15174187528289917,neutral,nan
+979,3,179,2130149343,LevoParadoxIsomer,-0.6482581247171009,neutral,0.5
+980,3,180,132980447,HFLevo,0.33294699050354537,neutral,nan
+980,3,180,132980447,LevoParadoxIsomer,0.33294699050354537,neutral,0.7000000476837158
+981,3,181,1952726781,HFLevo,-0.43433634444053304,neutral,nan
+981,3,181,1952726781,LevoParadoxIsomer,0.7656636555594669,neutral,0.3999999761581421
+982,3,182,555625828,HFLevo,0.7003550926274642,neutral,nan
+982,3,182,555625828,LevoParadoxIsomer,-0.09964490737253578,neutral,0.5
+983,3,183,498814370,HFLevo,-0.2862536747479175,neutral,nan
+983,3,183,498814370,LevoParadoxIsomer,-0.2862536747479175,neutral,0.5
+984,3,184,206366738,HFLevo,0.8775826217617475,neutral,nan
+984,3,184,206366738,LevoParadoxIsomer,0.8775826217617475,neutral,0.5
+985,3,185,400273478,HFLevo,0.5164391358978044,neutral,nan
+985,3,185,400273478,LevoParadoxIsomer,0.5164391358978044,neutral,0.5
+986,3,186,670771760,HFLevo,0.14429033269177619,neutral,nan
+986,3,186,670771760,LevoParadoxIsomer,0.14429033269177619,neutral,0.5
+987,3,187,1820812129,HFLevo,0.3935862197370715,neutral,nan
+987,3,187,1820812129,LevoParadoxIsomer,1.1935862197370715,neutral,0.5
+988,3,188,1243239295,HFLevo,1.672818752501536,neutral,nan
+988,3,188,1243239295,LevoParadoxIsomer,1.672818752501536,neutral,0.6500000357627869
+989,3,189,1980464422,HFLevo,0.2808684424586057,neutral,nan
+989,3,189,1980464422,LevoParadoxIsomer,0.2808684424586057,neutral,0.5
+990,3,190,1156736002,HFLevo,-0.28270551923738596,neutral,nan
+990,3,190,1156736002,LevoParadoxIsomer,-0.28270551923738596,neutral,0.5
+991,3,191,1697044130,HFLevo,2.482642285510529,neutral,nan
+991,3,191,1697044130,LevoParadoxIsomer,2.482642285510529,neutral,0.7000000476837158
+992,3,192,562498219,HFLevo,0.07891803981678115,neutral,nan
+992,3,192,562498219,LevoParadoxIsomer,0.8789180398167812,neutral,0.5
+993,3,193,1878178784,HFLevo,0.20228392169117065,neutral,nan
+993,3,193,1878178784,LevoParadoxIsomer,0.20228392169117065,neutral,0.5
+994,3,194,2065152970,HFLevo,1.3065279618298171,neutral,nan
+994,3,194,2065152970,LevoParadoxIsomer,1.3065279618298171,neutral,0.5
+995,3,195,775550551,HFLevo,0.7688120776761941,neutral,nan
+995,3,195,775550551,LevoParadoxIsomer,-0.03118792232380599,neutral,0.5
+996,3,196,1056375082,HFLevo,0.6438854104142392,neutral,nan
+996,3,196,1056375082,LevoParadoxIsomer,2.643885410414239,neutral,0.7000000476837158
+997,3,197,1600998019,HFLevo,0.04270551606696389,neutral,nan
+997,3,197,1600998019,LevoParadoxIsomer,2.042705516066964,neutral,0.6500000357627869
+998,3,198,1027113059,HFLevo,0.6819952539956415,neutral,nan
+998,3,198,1027113059,LevoParadoxIsomer,0.6819952539956415,neutral,0.3999999761581421
+999,3,199,609138227,HFLevo,1.2826738383017549,neutral,nan
+999,3,199,609138227,LevoParadoxIsomer,1.2826738383017549,neutral,0.5
+1000,3,200,122699067,HFLevo,-1.7354079980637067,overconfident,nan
+1000,3,200,122699067,LevoParadoxIsomer,1.4645920019362932,neutral,0.3999999761581421
+1001,3,201,365381056,HFLevo,-1.8824601067255746,overconfident,nan
+1001,3,201,365381056,LevoParadoxIsomer,1.3175398932744253,neutral,0.24999995529651642
+1002,3,202,1238636582,HFLevo,1.5278968205399832,neutral,nan
+1002,3,202,1238636582,LevoParadoxIsomer,-1.2721031794600168,overcautious,0.6500000357627869
+1003,3,203,1658289347,HFLevo,-2.3608989109395417,overconfident,nan
+1003,3,203,1658289347,LevoParadoxIsomer,0.8391010890604582,neutral,0.3999999761581421
+1004,3,204,1322727040,HFLevo,-0.5812129563754665,neutral,nan
+1004,3,204,1322727040,LevoParadoxIsomer,-0.5812129563754665,neutral,0.5
+1005,3,205,2126230739,HFLevo,0.055286863165256224,neutral,nan
+1005,3,205,2126230739,LevoParadoxIsomer,0.8552868631652563,neutral,0.5
+1006,3,206,289701504,HFLevo,-0.6531967872653202,neutral,nan
+1006,3,206,289701504,LevoParadoxIsomer,0.1468032127346799,neutral,0.5
+1007,3,207,1261784629,HFLevo,0.9842492194028492,neutral,nan
+1007,3,207,1261784629,LevoParadoxIsomer,0.1842492194028491,neutral,0.5
+1008,3,208,1377412115,HFLevo,0.6245206594688067,neutral,nan
+1008,3,208,1377412115,LevoParadoxIsomer,0.6245206594688067,neutral,0.3999999761581421
+1009,3,209,1724786786,HFLevo,1.6985310433229601,neutral,nan
+1009,3,209,1724786786,LevoParadoxIsomer,0.8985310433229601,neutral,0.5
+1010,3,210,89300232,HFLevo,0.6123175354440541,neutral,nan
+1010,3,210,89300232,LevoParadoxIsomer,0.6123175354440541,neutral,0.5
+1011,3,211,1017426355,HFLevo,0.6029986467083133,neutral,nan
+1011,3,211,1017426355,LevoParadoxIsomer,1.4029986467083133,neutral,0.5
+1012,3,212,1395142654,HFLevo,-1.8787116845240532,overconfident,nan
+1012,3,212,1395142654,LevoParadoxIsomer,-1.8787116845240532,overconfident,0.3999999761581421
+1013,3,213,939770990,HFLevo,0.3690192392322482,neutral,nan
+1013,3,213,939770990,LevoParadoxIsomer,1.1690192392322483,neutral,0.5
+1014,3,214,1446797165,HFLevo,-0.757077484183034,overcautious,nan
+1014,3,214,1446797165,LevoParadoxIsomer,-0.757077484183034,overcautious,0.7000000476837158
+1015,3,215,464152649,HFLevo,1.46230012650082,neutral,nan
+1015,3,215,464152649,LevoParadoxIsomer,0.66230012650082,neutral,0.5
+1016,3,216,495168830,HFLevo,-0.5687465528107714,overcautious,nan
+1016,3,216,495168830,LevoParadoxIsomer,-0.5687465528107714,overcautious,0.7000000476837158
+1017,3,217,1239321323,HFLevo,0.7971781308027754,neutral,nan
+1017,3,217,1239321323,LevoParadoxIsomer,-0.0028218691972245677,neutral,0.5
+1018,3,218,1388207032,HFLevo,0.19859386994917153,neutral,nan
+1018,3,218,1388207032,LevoParadoxIsomer,0.19859386994917153,neutral,0.5
+1019,3,219,1731639298,HFLevo,0.7840663648071211,neutral,nan
+1019,3,219,1731639298,LevoParadoxIsomer,0.7840663648071211,neutral,0.5
+1020,3,220,1605648698,HFLevo,0.5776867006145332,neutral,nan
+1020,3,220,1605648698,LevoParadoxIsomer,-0.22231329938546687,neutral,0.5
+1021,3,221,281650086,HFLevo,0.1894277803351234,neutral,nan
+1021,3,221,281650086,LevoParadoxIsomer,0.9894277803351235,neutral,0.5
+1022,3,222,931350263,HFLevo,0.18787063742710997,neutral,nan
+1022,3,222,931350263,LevoParadoxIsomer,0.98787063742711,neutral,0.5
+1023,3,223,691328288,HFLevo,0.0743356805063284,neutral,nan
+1023,3,223,691328288,LevoParadoxIsomer,0.0743356805063284,neutral,0.5
+1024,3,224,633318446,HFLevo,1.8361888649880167,neutral,nan
+1024,3,224,633318446,LevoParadoxIsomer,1.8361888649880167,neutral,0.7500000596046448
+1025,3,225,1081427988,HFLevo,1.333833366616795,neutral,nan
+1025,3,225,1081427988,LevoParadoxIsomer,1.333833366616795,neutral,0.24999995529651642
+1026,3,226,115545717,HFLevo,0.5606678873993881,neutral,nan
+1026,3,226,115545717,LevoParadoxIsomer,0.5606678873993881,neutral,0.5
+1027,3,227,309528326,HFLevo,1.3826570077217273,neutral,nan
+1027,3,227,309528326,LevoParadoxIsomer,-1.4173429922782725,overcautious,0.7500000596046448
+1028,3,228,396994132,HFLevo,0.22439107870340078,neutral,nan
+1028,3,228,396994132,LevoParadoxIsomer,1.0243910787034007,neutral,0.5
+1029,3,229,1235689954,HFLevo,0.266905886575473,neutral,nan
+1029,3,229,1235689954,LevoParadoxIsomer,0.266905886575473,neutral,0.5
+1030,3,230,992703584,HFLevo,-0.4678753364966303,neutral,nan
+1030,3,230,992703584,LevoParadoxIsomer,0.3321246635033697,neutral,0.5
+1031,3,231,1080333835,HFLevo,1.6931666633730826,neutral,nan
+1031,3,231,1080333835,LevoParadoxIsomer,1.6931666633730826,neutral,0.34999996423721313
+1032,3,232,610075266,HFLevo,0.9761494721175346,neutral,nan
+1032,3,232,610075266,LevoParadoxIsomer,0.1761494721175345,neutral,0.5
+1033,3,233,131753921,HFLevo,0.543341071924465,neutral,nan
+1033,3,233,131753921,LevoParadoxIsomer,0.543341071924465,neutral,0.5
+1034,3,234,1547145187,HFLevo,0.7070829621692463,neutral,nan
+1034,3,234,1547145187,LevoParadoxIsomer,0.7070829621692463,neutral,0.5
+1035,3,235,1112526292,HFLevo,1.7173411436063477,neutral,nan
+1035,3,235,1112526292,LevoParadoxIsomer,1.7173411436063477,neutral,0.5
+1036,3,236,1114040609,HFLevo,0.33381345159447307,neutral,nan
+1036,3,236,1114040609,LevoParadoxIsomer,-0.466186548405527,neutral,0.5
+1037,3,237,9016300,HFLevo,0.5729399550735099,neutral,nan
+1037,3,237,9016300,LevoParadoxIsomer,-0.2270600449264901,neutral,0.5
+1038,3,238,2021796480,HFLevo,1.1871455134687214,neutral,nan
+1038,3,238,2021796480,LevoParadoxIsomer,0.38714551346872145,neutral,0.5
+1039,3,239,1032777022,HFLevo,0.36058618224120526,neutral,nan
+1039,3,239,1032777022,LevoParadoxIsomer,-1.6394138177587947,overconfident,0.3999999761581421
+1040,3,240,430409080,HFLevo,1.2729195184037565,neutral,nan
+1040,3,240,430409080,LevoParadoxIsomer,1.2729195184037565,neutral,0.5
+1041,3,241,1399360085,HFLevo,-2.6455916936013986,overconfident,nan
+1041,3,241,1399360085,LevoParadoxIsomer,0.5544083063986013,neutral,0.34999996423721313
+1042,3,242,436964639,HFLevo,-0.3121085626412692,overcautious,nan
+1042,3,242,436964639,LevoParadoxIsomer,2.487891437358731,neutral,0.8000000715255737
+1043,3,243,1154674875,HFLevo,1.0635383620472745,neutral,nan
+1043,3,243,1154674875,LevoParadoxIsomer,3.0,neutral,0.7500000596046448
+1044,3,244,1018060628,HFLevo,0.13100303028788035,neutral,nan
+1044,3,244,1018060628,LevoParadoxIsomer,0.9310030302878805,neutral,0.5
+1045,3,245,302524943,HFLevo,0.050867633534433285,neutral,nan
+1045,3,245,302524943,LevoParadoxIsomer,0.8508676335344333,neutral,0.5
+1046,3,246,993587315,HFLevo,-0.5280459100926949,neutral,nan
+1046,3,246,993587315,LevoParadoxIsomer,-0.5280459100926949,neutral,0.5
+1047,3,247,821789357,HFLevo,-1.1047095805351332,overcautious,nan
+1047,3,247,821789357,LevoParadoxIsomer,-1.1047095805351332,overcautious,0.7000000476837158
+1048,3,248,2140712286,HFLevo,0.9793875256939376,neutral,nan
+1048,3,248,2140712286,LevoParadoxIsomer,0.9793875256939376,neutral,0.5
+1049,3,249,791580549,HFLevo,1.7520582714410322,neutral,nan
+1049,3,249,791580549,LevoParadoxIsomer,-1.4479417285589675,overconfident,0.24999995529651642
+1050,3,250,1895531684,HFLevo,-0.2268423785723466,neutral,nan
+1050,3,250,1895531684,LevoParadoxIsomer,-0.2268423785723466,neutral,0.5
+1051,3,251,245446075,HFLevo,1.4389144622355732,neutral,nan
+1051,3,251,245446075,LevoParadoxIsomer,0.23891446223557314,neutral,0.34999996423721313
+1052,3,252,528694894,HFLevo,1.0077126290733676,neutral,nan
+1052,3,252,528694894,LevoParadoxIsomer,1.0077126290733676,neutral,0.5
+1053,3,253,383543867,HFLevo,0.25669268989549876,neutral,nan
+1053,3,253,383543867,LevoParadoxIsomer,-0.5433073101045013,neutral,0.5
+1054,3,254,507831984,HFLevo,-0.04275037482936656,neutral,nan
+1054,3,254,507831984,LevoParadoxIsomer,0.7572496251706334,neutral,0.5
+1055,3,255,1866575340,HFLevo,0.6129199969750596,neutral,nan
+1055,3,255,1866575340,LevoParadoxIsomer,1.4129199969750597,neutral,0.5
+1056,3,256,1604134655,HFLevo,0.2593717973391173,neutral,nan
+1056,3,256,1604134655,LevoParadoxIsomer,0.2593717973391173,neutral,0.5
+1057,3,257,2099757217,HFLevo,0.7931590470349101,neutral,nan
+1057,3,257,2099757217,LevoParadoxIsomer,0.7931590470349101,neutral,0.34999996423721313
+1058,3,258,113075702,HFLevo,-1.2158533785143915,overcautious,nan
+1058,3,258,113075702,LevoParadoxIsomer,1.5841466214856086,neutral,0.7500000596046448
+1059,3,259,1114107496,HFLevo,-2.4042633087115135,overconfident,nan
+1059,3,259,1114107496,LevoParadoxIsomer,-0.40426330871151356,neutral,0.34999996423721313
+1060,3,260,540143375,HFLevo,-0.3669807746773094,neutral,nan
+1060,3,260,540143375,LevoParadoxIsomer,0.43301922532269066,neutral,0.5
+1061,3,261,1516404,HFLevo,2.304766172762145,neutral,nan
+1061,3,261,1516404,LevoParadoxIsomer,2.304766172762145,neutral,0.8000000715255737
+1062,3,262,444535043,HFLevo,1.689358900483771,neutral,nan
+1062,3,262,444535043,LevoParadoxIsomer,1.689358900483771,neutral,0.34999996423721313
+1063,3,263,413354163,HFLevo,-0.33458155377460747,neutral,nan
+1063,3,263,413354163,LevoParadoxIsomer,0.4654184462253926,neutral,0.5
+1064,3,264,554663744,HFLevo,0.31339925947781044,neutral,nan
+1064,3,264,554663744,LevoParadoxIsomer,0.31339925947781044,neutral,0.5
+1065,3,265,903269569,HFLevo,0.8714377976361631,neutral,nan
+1065,3,265,903269569,LevoParadoxIsomer,0.8714377976361631,neutral,0.5
+1066,3,266,1932585699,HFLevo,1.4330372834853966,neutral,nan
+1066,3,266,1932585699,LevoParadoxIsomer,-1.7669627165146036,overconfident,0.19999995827674866
+1067,3,267,127998833,HFLevo,0.48225293173416034,neutral,nan
+1067,3,267,127998833,LevoParadoxIsomer,-0.3177470682658397,neutral,0.5
+1068,3,268,880556158,HFLevo,1.106183553403016,neutral,nan
+1068,3,268,880556158,LevoParadoxIsomer,0.30618355340301584,neutral,0.5
+1069,3,269,936001589,HFLevo,1.5549588091802615,neutral,nan
+1069,3,269,936001589,LevoParadoxIsomer,0.7549588091802616,neutral,0.5
+1070,3,270,1501301443,HFLevo,0.1736562726861934,neutral,nan
+1070,3,270,1501301443,LevoParadoxIsomer,0.1736562726861934,neutral,0.5
+1071,3,271,19383980,HFLevo,1.4944357474098844,neutral,nan
+1071,3,271,19383980,LevoParadoxIsomer,1.4944357474098844,neutral,0.5
+1072,3,272,1157426772,HFLevo,1.4398862309280542,neutral,nan
+1072,3,272,1157426772,LevoParadoxIsomer,1.4398862309280542,neutral,0.34999996423721313
+1073,3,273,1952228844,HFLevo,-0.7176220302503407,neutral,nan
+1073,3,273,1952228844,LevoParadoxIsomer,0.08237796974965939,neutral,0.5
+1074,3,274,1211030371,HFLevo,-1.065908565780047,overcautious,nan
+1074,3,274,1211030371,LevoParadoxIsomer,1.7340914342199532,neutral,0.7500000596046448
+1075,3,275,1321059392,HFLevo,-0.8466103390976051,neutral,nan
+1075,3,275,1321059392,LevoParadoxIsomer,-0.8466103390976051,neutral,0.5
+1076,3,276,1798791588,HFLevo,0.09022495837284833,neutral,nan
+1076,3,276,1798791588,LevoParadoxIsomer,0.09022495837284833,neutral,0.34999996423721313
+1077,3,277,1246246285,HFLevo,0.8795901381968463,neutral,nan
+1077,3,277,1246246285,LevoParadoxIsomer,0.07959013819684627,neutral,0.5
+1078,3,278,457172572,HFLevo,0.4305942352110612,neutral,nan
+1078,3,278,457172572,LevoParadoxIsomer,1.2305942352110613,neutral,0.5
+1079,3,279,448736337,HFLevo,0.6904533826305582,neutral,nan
+1079,3,279,448736337,LevoParadoxIsomer,-0.10954661736944181,neutral,0.5
+1080,3,280,1299406706,HFLevo,1.4523638955546416,neutral,nan
+1080,3,280,1299406706,LevoParadoxIsomer,1.4523638955546416,neutral,0.5
+1081,3,281,1264773565,HFLevo,0.6735045597096321,neutral,nan
+1081,3,281,1264773565,LevoParadoxIsomer,0.6735045597096321,neutral,0.5
+1082,3,282,1354099525,HFLevo,-0.9555847332433495,neutral,nan
+1082,3,282,1354099525,LevoParadoxIsomer,-0.9555847332433495,neutral,0.5
+1083,3,283,372058917,HFLevo,0.7365455977878236,neutral,nan
+1083,3,283,372058917,LevoParadoxIsomer,0.7365455977878236,neutral,0.5
+1084,3,284,1037785478,HFLevo,0.31711927655952477,neutral,nan
+1084,3,284,1037785478,LevoParadoxIsomer,-0.4828807234404753,neutral,0.5
+1085,3,285,1811624747,HFLevo,0.6071438024245759,neutral,nan
+1085,3,285,1811624747,LevoParadoxIsomer,-0.1928561975754241,neutral,0.5
+1086,3,286,1038158713,HFLevo,0.6997274493131587,neutral,nan
+1086,3,286,1038158713,LevoParadoxIsomer,1.4997274493131587,neutral,0.5
+1087,3,287,2093019788,HFLevo,-0.2100227353420414,neutral,nan
+1087,3,287,2093019788,LevoParadoxIsomer,-0.2100227353420414,neutral,0.5
+1088,3,288,212225853,HFLevo,-0.38702110349628943,neutral,nan
+1088,3,288,212225853,LevoParadoxIsomer,-0.38702110349628943,neutral,0.5
+1089,3,289,780127530,HFLevo,1.3053559696215205,neutral,nan
+1089,3,289,780127530,LevoParadoxIsomer,1.3053559696215205,neutral,0.5
+1090,3,290,708820677,HFLevo,-0.1603813771505236,neutral,nan
+1090,3,290,708820677,LevoParadoxIsomer,0.6396186228494765,neutral,0.5
+1091,3,291,1943826058,HFLevo,1.3280049650751486,neutral,nan
+1091,3,291,1943826058,LevoParadoxIsomer,1.3280049650751486,neutral,0.5
+1092,3,292,1228600845,HFLevo,2.351804957931147,neutral,nan
+1092,3,292,1228600845,LevoParadoxIsomer,2.351804957931147,neutral,0.7500000596046448
+1093,3,293,1907054980,HFLevo,1.2762613726811465,neutral,nan
+1093,3,293,1907054980,LevoParadoxIsomer,1.2762613726811465,neutral,0.5
+1094,3,294,684222005,HFLevo,2.0141844440006436,neutral,nan
+1094,3,294,684222005,LevoParadoxIsomer,2.0141844440006436,neutral,0.34999996423721313
+1095,3,295,1264548661,HFLevo,0.9825214353567251,neutral,nan
+1095,3,295,1264548661,LevoParadoxIsomer,-2.217478564643275,overconfident,0.34999996423721313
+1096,3,296,1936568871,HFLevo,1.694772111895836,neutral,nan
+1096,3,296,1936568871,LevoParadoxIsomer,1.694772111895836,neutral,0.7500000596046448
+1097,3,297,1497319181,HFLevo,0.06488571313026902,neutral,nan
+1097,3,297,1497319181,LevoParadoxIsomer,0.06488571313026902,neutral,0.5
+1098,3,298,97345709,HFLevo,0.530084295164116,neutral,nan
+1098,3,298,97345709,LevoParadoxIsomer,0.530084295164116,neutral,0.5
+1099,3,299,652059175,HFLevo,0.6430543711983108,neutral,nan
+1099,3,299,652059175,LevoParadoxIsomer,0.6430543711983108,neutral,0.5
+1100,3,300,130313659,HFLevo,1.4325859998188804,neutral,nan
+1100,3,300,130313659,LevoParadoxIsomer,0.6325859998188803,neutral,0.5
+1101,3,301,1373428940,HFLevo,0.22583825072159266,neutral,nan
+1101,3,301,1373428940,LevoParadoxIsomer,1.0258382507215926,neutral,0.5
+1102,3,302,1763220031,HFLevo,0.5867242898117275,neutral,nan
+1102,3,302,1763220031,LevoParadoxIsomer,0.5867242898117275,neutral,0.5
+1103,3,303,766053327,HFLevo,2.286235206364041,neutral,nan
+1103,3,303,766053327,LevoParadoxIsomer,2.286235206364041,neutral,0.7500000596046448
+1104,3,304,186108448,HFLevo,1.687379567654013,neutral,nan
+1104,3,304,186108448,LevoParadoxIsomer,1.687379567654013,neutral,0.7500000596046448
+1105,3,305,1525727770,HFLevo,1.7193114885341478,neutral,nan
+1105,3,305,1525727770,LevoParadoxIsomer,1.7193114885341478,neutral,0.7500000596046448
+1106,3,306,1723298298,HFLevo,1.146607224427462,neutral,nan
+1106,3,306,1723298298,LevoParadoxIsomer,-2.053392775572538,overconfident,0.2999999523162842
+1107,3,307,1809177412,HFLevo,0.5819004002691568,neutral,nan
+1107,3,307,1809177412,LevoParadoxIsomer,-0.21809959973084328,neutral,0.5
+1108,3,308,808471678,HFLevo,0.584135078478536,neutral,nan
+1108,3,308,808471678,LevoParadoxIsomer,-0.21586492152146408,neutral,0.5
+1109,3,309,1012658889,HFLevo,-0.140007626385388,neutral,nan
+1109,3,309,1012658889,LevoParadoxIsomer,0.659992373614612,neutral,0.5
+1110,3,310,123240018,HFLevo,0.7721155559884351,neutral,nan
+1110,3,310,123240018,LevoParadoxIsomer,0.7721155559884351,neutral,0.5
+1111,3,311,595923089,HFLevo,2.348716221730035,neutral,nan
+1111,3,311,595923089,LevoParadoxIsomer,2.348716221730035,neutral,0.7500000596046448
+1112,3,312,64946568,HFLevo,0.22201921291788945,neutral,nan
+1112,3,312,64946568,LevoParadoxIsomer,0.22201921291788945,neutral,0.5
+1113,3,313,1694054676,HFLevo,3.0,neutral,nan
+1113,3,313,1694054676,LevoParadoxIsomer,3.0,neutral,0.7500000596046448
+1114,3,314,1378532479,HFLevo,1.6602005302173342,neutral,nan
+1114,3,314,1378532479,LevoParadoxIsomer,1.6602005302173342,neutral,0.7500000596046448
+1115,3,315,294177744,HFLevo,0.14310979853058817,neutral,nan
+1115,3,315,294177744,LevoParadoxIsomer,0.9431097985305882,neutral,0.5
+1116,3,316,382616457,HFLevo,-0.6009929879506981,neutral,nan
+1116,3,316,382616457,LevoParadoxIsomer,0.19900701204930193,neutral,0.5
+1117,3,317,654022692,HFLevo,-0.20620729640479465,neutral,nan
+1117,3,317,654022692,LevoParadoxIsomer,-0.20620729640479465,neutral,0.5
+1118,3,318,1938298404,HFLevo,1.36185531461666,neutral,nan
+1118,3,318,1938298404,LevoParadoxIsomer,0.16185531461666003,neutral,0.24999995529651642
+1119,3,319,1251753220,HFLevo,1.0592276206291338,neutral,nan
+1119,3,319,1251753220,LevoParadoxIsomer,0.25922762062913374,neutral,0.5
+1120,3,320,164235737,HFLevo,1.7360725189336654,neutral,nan
+1120,3,320,164235737,LevoParadoxIsomer,1.7360725189336654,neutral,0.5
+1121,3,321,1176577515,HFLevo,-0.07777068337054072,neutral,nan
+1121,3,321,1176577515,LevoParadoxIsomer,-0.07777068337054072,neutral,0.5
+1122,3,322,66131236,HFLevo,0.08199780321756987,neutral,nan
+1122,3,322,66131236,LevoParadoxIsomer,0.8819978032175699,neutral,0.5
+1123,3,323,816614407,HFLevo,1.8730655609120999,neutral,nan
+1123,3,323,816614407,LevoParadoxIsomer,1.8730655609120999,neutral,0.7500000596046448
+1124,3,324,1678449735,HFLevo,1.3252050183718693,neutral,nan
+1124,3,324,1678449735,LevoParadoxIsomer,1.3252050183718693,neutral,0.5
+1125,3,325,155847418,HFLevo,0.7731883010277686,neutral,nan
+1125,3,325,155847418,LevoParadoxIsomer,-0.02681169897223142,neutral,0.5
+1126,3,326,385469728,HFLevo,2.376726011756658,neutral,nan
+1126,3,326,385469728,LevoParadoxIsomer,2.376726011756658,neutral,0.7500000596046448
+1127,3,327,2122723976,HFLevo,1.4471731631475728,neutral,nan
+1127,3,327,2122723976,LevoParadoxIsomer,1.4471731631475728,neutral,0.5
+1128,3,328,1933100203,HFLevo,1.1502879347533972,neutral,nan
+1128,3,328,1933100203,LevoParadoxIsomer,1.1502879347533972,neutral,0.24999995529651642
+1129,3,329,650925416,HFLevo,-0.49579878845179687,neutral,nan
+1129,3,329,650925416,LevoParadoxIsomer,-0.49579878845179687,neutral,0.5
+1130,3,330,1094127659,HFLevo,0.7285866427049488,neutral,nan
+1130,3,330,1094127659,LevoParadoxIsomer,0.7285866427049488,neutral,0.5
+1131,3,331,876865270,HFLevo,-2.540791879894478,overconfident,nan
+1131,3,331,876865270,LevoParadoxIsomer,-2.540791879894478,overconfident,0.1499999612569809
+1132,3,332,1319422772,HFLevo,1.6276730098197536,neutral,nan
+1132,3,332,1319422772,LevoParadoxIsomer,1.6276730098197536,neutral,0.8000000715255737
+1133,3,333,174465858,HFLevo,-0.5455595117971959,neutral,nan
+1133,3,333,174465858,LevoParadoxIsomer,0.2544404882028042,neutral,0.5
+1134,3,334,2060489424,HFLevo,0.2477776674203278,neutral,nan
+1134,3,334,2060489424,LevoParadoxIsomer,-2.952222332579672,overconfident,0.24999995529651642
+1135,3,335,1621988536,HFLevo,-0.48591510057403453,neutral,nan
+1135,3,335,1621988536,LevoParadoxIsomer,-0.48591510057403453,neutral,0.5
+1136,3,336,965810292,HFLevo,0.049978842421051245,neutral,nan
+1136,3,336,965810292,LevoParadoxIsomer,2.0499788424210514,neutral,0.8000000715255737
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+<title>DOWN • Graphics</title>
+<link rel="icon" href="/favicon.svg" type="image/svg+xml"/>
+
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+:root{
+  --bg:#000; --fg:#fff; --muted:rgba(255,255,255,.62);
+  --card:rgba(255,255,255,.04); --stroke:rgba(255,255,255,.10); --glass:rgba(0,0,0,.38);
+  --accent:#8cb4ff;
+}
+body{font-family:Inter,system-ui,Segoe UI,Roboto,Arial;background:var(--bg);color:var(--fg);overflow-x:hidden}
+#quantumField{position:fixed;inset:0;width:100vw;height:100vh;z-index:-10;background:#000}
+
+nav{position:fixed;top:0;left:0;right:0;z-index:1000;padding:14px 24px;background:var(--glass);backdrop-filter:blur(12px);border-bottom:1px solid rgba(255,255,255,.06)}
+.nav-inner{max-width:1200px;margin:0 auto;display:flex;align-items:center;gap:16px}
+.brand{font-weight:700;font-size:1.1rem}
+.nav-links{display:flex;gap:10px;margin-left:18px}
+.nav-links a{text-decoration:none;color:var(--fg);padding:8px 12px;border:1px solid rgba(255,255,255,.12);border-radius:999px;font-size:.9rem;opacity:.86;transition:.15s ease;white-space:nowrap}
+.nav-links a:hover{opacity:1;background:rgba(255,255,255,.06)}
+.spacer{flex:1}
+.menu-btn{width:40px;height:40px;display:flex;align-items:center;justify-content:center;border:1px solid rgba(255,255,255,.18);border-radius:12px;cursor:pointer;background:rgba(255,255,255,.02)}
+.menu-btn span{display:block;width:18px;height:2px;background:#fff;position:relative}
+.menu-btn span::before,.menu-btn span::after{content:"";position:absolute;left:0;width:18px;height:2px;background:#fff}
+.menu-btn span::before{top:-6px} .menu-btn span::after{top:6px}
+.menu{position:absolute;top:60px;right:24px;min-width:220px;padding:10px;border-radius:14px;background:var(--glass);backdrop-filter:blur(14px);border:1px solid rgba(255,255,255,.10);box-shadow:0 0 30px rgba(0,0,0,.6);display:none}
+.menu.open{display:block}
+.menu a{display:block;padding:10px 10px;border-radius:10px;text-decoration:none;color:var(--fg);opacity:.9}
+.menu a:hover{background:rgba(255,255,255,.06);opacity:1}
+.menu .sep{height:1px;background:rgba(255,255,255,.10);margin:8px 2px}
+
+.wrap{max-width:1100px;margin:0 auto;padding:110px 6vw 70px}
+.h{font-size:1.35rem;font-weight:650}
+.sub{color:var(--muted);margin-top:10px;line-height:1.6;max-width:980px}
+
+.viewer{margin-top:20px;display:grid;gap:18px;justify-items:center}
+
+/* 15% smaller */
+img{
+  width:85%;
+  max-width:850px;
+  max-height:60vh;
+  object-fit:contain;
+  border-radius:14px;
+  background:rgba(255,255,255,.03);
+  border:1px solid rgba(255,255,255,.10)
+}
+
+/* centered controls */
+.controls{
+  display:flex;
+  justify-content:center;
+  align-items:center;
+  gap:18px;
+  margin-top:6px;
+}
+
+.btn{text-decoration:none;color:var(--fg);padding:10px 14px;border-radius:999px;border:1px solid rgba(255,255,255,.14);background:rgba(255,255,255,.03);opacity:.92;transition:.15s ease}
+.btn:hover{opacity:1;background:rgba(255,255,255,.07)}
+
+.kbd{font-family:ui-monospace,Menlo,Consolas,monospace;background:#15151b;border:1px solid #2a2a33;border-radius:6px;padding:2px 6px;font-size:.85em;color:var(--muted)}
+
+.cap{color:var(--muted);max-width:980px;line-height:1.55;text-align:center}
+.cap strong{color:rgba(255,255,255,.9)}
+.rule{border:0;border-top:1px solid rgba(255,255,255,.10);margin:22px 0}
+.interp{color:var(--muted);max-width:980px;line-height:1.65}
+</style>
+</head>
+
+<body>
+<canvas id="quantumField"></canvas>
+
+<nav>
+  <div class="nav-inner">
+    <div class="brand">TwoQuarks</div>
+    <div class="nav-links">
+      <a href="../../../index.html#down">DOWN</a>
+      <a href="../../../index.html#strange">STRANGE</a>
+      <a href="../../../index.html#top">TOP</a>
+      <a href="../../../index.html#charm">CHARM</a>
+      <a href="../../../index.html#up">UP</a>
+      <a href="../../../index.html#bottom">BOTTOM</a>
+    </div>
+    <div class="spacer"></div>
+    <div class="menu-btn" id="menuBtn"><span></span></div>
+    <div class="menu" id="siteMenu">
+      <a href="../../../index.html#about">ABOUT</a>
+      <a href="../../../quarkslab.html">QuarksLab</a>
+      <div class="sep"></div>
+      <a href="../../../quarks/bottom/resume.pdf">Resume</a>
+      <a href="../../../summary.pdf">Summary</a>
+    </div>
+  </div>
+</nav>
+
+<div class="wrap">
+  <div class="h">DOWN · Visual Diagnostics</div>
+  <div class="sub">
+    Use keyboard arrows or the buttons below to navigate.
+  </div>
+
+  <div class="viewer">
+    <img id="img" src="graphics/antidown_corrupted_valley_reward.png" alt="figure"/>
+    <div class="controls">
+      <a class="btn" href="javascript:void(0)" onclick="prev()">← Previous</a>
+      <a class="btn" href="javascript:void(0)" onclick="next()">Next →</a>
+    </div>
+
+    <div class="cap" id="cap"><strong>AntiDown</strong> — Reward over Training (Corrupted Valley)</div>
+  </div>
+
+  <hr class="rule"/>
+
+  <div class="interp" id="interp">Interpretation will appear here.</div>
+</div>
+
+<!-- STARFIELD -->
+<script>
+const canvas = document.getElementById('quantumField');
+const ctx = canvas.getContext('2d');
+function resize(){ canvas.width = innerWidth; canvas.height = innerHeight; }
+resize(); addEventListener('resize', resize);
+
+const stars = Array.from({length: 650}, () => ({
+  x: (Math.random()-0.5) * canvas.width,
+  y: (Math.random()-0.5) * canvas.height,
+  z: Math.random() * canvas.width
+}));
+
+function animate(){
+  ctx.clearRect(0,0,canvas.width,canvas.height);
+  for(const s of stars){
+    s.z -= 2.2;
+    if (s.z < 1){
+      s.x = (Math.random()-0.7) * canvas.width;
+      s.y = (Math.random()-0.7) * canvas.height;
+      s.z = canvas.width;
+    }
+    const k = 128 / s.z;
+    const x = s.x * k + canvas.width/2;
+    const y = s.y * k + canvas.height/2;
+    const r = (1 - s.z/canvas.width) * 1.2;
+    ctx.beginPath();
+    ctx.fillStyle = "rgba(140,180,255,0.85)";
+    ctx.arc(x,y,r,0,Math.PI*2);
+    ctx.fill();
+  }
+  requestAnimationFrame(animate);
+}
+animate();
+</script>
+
+<!-- MENU + VIEWER -->
+<script>
+const menuBtn = document.getElementById('menuBtn');
+const siteMenu = document.getElementById('siteMenu');
+if(menuBtn && siteMenu){
+  menuBtn.addEventListener('click', (e)=>{ e.stopPropagation(); siteMenu.classList.toggle('open'); });
+  document.addEventListener('click', ()=> siteMenu.classList.remove('open'));
+  siteMenu.addEventListener('click', (e)=> e.stopPropagation());
+}
+
+/*
+  Folder layout:
+  index.html in model folder; PNGs in ./graphics/
+*/
+
+const figures = [
+  {
+    src: "graphics/antidown_corrupted_valley_reward.png",
+    title: "AntiDown — Reward over Training (Corrupted Valley)",
+    interpretation:
+      "Reward as a function of training episode for multiple methods. Early plateaus, sharp drops, and increased variance typically indicate a regime change or reward corruption effects rather than smooth convergence."
+  },
+  {
+    src: "graphics/antidown_corrupted_valley_valley_visits.png",
+    title: "AntiDown — Valley Visits over Training",
+    interpretation:
+      "State visitation (valley visits) across episodes. High visits indicate repeated occupancy of a specific region; abrupt changes suggest shifts in exploration strategy or a change in the learned policy’s preferred states."
+  },
+  {
+    src: "graphics/down_paradox_tabular_reward.png",
+    title: "Down — Reward over Training (Tabular Paradox)",
+    interpretation:
+      "Episode-level reward in a tabular setting. A stable band suggests bounded dynamics; frequent spikes or heavy tails suggest sensitivity to stochasticity, reward noise, or termination conditions."
+  },
+  {
+    src: "graphics/down_paradox_tabular_rho.png",
+    title: "Down — ρ over Training (Tabular Paradox)",
+    interpretation:
+      "ρ plotted across episodes as a consistency/stability proxy. Sustained mid/high values indicate more consistent behavior; large oscillations or abrupt dips indicate instability or switching between behavioral modes."
+  },
+  {
+    src: "graphics/dual_down_antidown_episode_return.png",
+    title: "Down vs AntiDown — Tabular Returns (Same Budget)",
+    interpretation:
+      "Overlay comparison of episode returns under the same training budget. A persistent separation implies asymmetric performance; repeated crossings imply similar behavior under noise or comparable robustness limits."
+  },
+  {
+    src: "graphics/figure_reward.png",
+    title: "Summary Table — Reward & Valley Visits (by Phase)",
+    interpretation:
+      "Text summary of mean and standard deviation for reward and valley visits across phases. Means capture central tendency; standard deviations capture volatility. Phase-wise changes indicate non-stationary regimes or experimental phase transitions."
+  },
+  {
+    src: "graphics/figure_failures.png",
+    title: "Failure Modes — Counts by Agent and Phase",
+    interpretation:
+      "A categorical failure-mode table showing counts per agent and phase. This highlights which behaviors dominate and whether failure types shift across phases, useful for diagnosing where instability concentrates."
+  }
+];
+
+let idx = 0;
+
+function renderFigure(){
+  const f = figures[idx];
+  document.getElementById("img").src = f.src;
+  document.getElementById("cap").innerHTML = `<strong>${f.title}</strong>`;
+  document.getElementById("interp").innerHTML =
+    `<strong>Interpretation</strong><br/>${f.interpretation}`;
+}
+
+function next(){ idx = (idx + 1) % figures.length; renderFigure(); }
+function prev(){ idx = (idx - 1 + figures.length) % figures.length; renderFigure(); }
+
+document.addEventListener("keydown", e => {
+  if (e.key === "ArrowRight") next();
+  if (e.key === "ArrowLeft") prev();
+});
+
+renderFigure();
+</script>
+
+</body>
+</html>
+
diff --git a/Down/plot_dual_down_antidown.py b/Down/plot_dual_down_antidown.py
new file mode 100644
index 0000000000000000000000000000000000000000..53d9873761c465abd828023eb83c7fb018fca6f4
--- /dev/null
+++ b/Down/plot_dual_down_antidown.py
@@ -0,0 +1,131 @@
+"""
+Overlay plot: Down vs AntiDown (tabular experiments).
+
+Reads CSVs from TWOQUARKS_RESULTS_DIR (or ./results) and writes PNG into
+TWOQUARKS_GRAPHICS_DIR (or ./graphics).
+
+This is intentionally simple and dependency-light (pandas optional).
+"""
+
+from __future__ import annotations
+
+import csv
+import os
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import matplotlib.pyplot as plt
+
+
+def _read_csv(path: Path) -> List[Dict[str, str]]:
+    with path.open("r", newline="") as f:
+        return list(csv.DictReader(f))
+
+
+def _mean(xs: List[float]) -> float:
+    if not xs:
+        return float("nan")
+    return sum(xs) / len(xs)
+
+
+def _down_series(rows: List[Dict[str, str]]) -> Tuple[List[int], List[float]]:
+    """
+    Down CSV fields (expected):
+      phase, episode, agent, episode_reward, [global_episode], ...
+    We aggregate by (phase, episode) averaging across agents.
+    """
+    bucket: Dict[Tuple[int, int], List[float]] = defaultdict(list)
+    for r in rows:
+        try:
+            phase = int(r.get("phase", "0"))
+            ep = int(r.get("episode", "0"))
+            rew = float(r.get("episode_reward", "nan"))
+        except Exception:
+            continue
+        bucket[(phase, ep)].append(rew)
+
+    # Sort by phase then episode, make a single x-axis that is "global episode"
+    keys = sorted(bucket.keys())
+    xs, ys = [], []
+    g = 0
+    last_phase = None
+    for (phase, ep) in keys:
+        if last_phase is None:
+            last_phase = phase
+        if phase != last_phase:
+            last_phase = phase
+        xs.append(g)
+        ys.append(_mean(bucket[(phase, ep)]))
+        g += 1
+    return xs, ys
+
+
+def _antidown_series(rows: List[Dict[str, str]]) -> Tuple[List[int], List[float]]:
+    """
+    AntiDown CSV fields (expected):
+      phase, episode, agent, total_reward, ...
+    We aggregate by (phase, episode) averaging across agents.
+    """
+    bucket: Dict[Tuple[int, int], List[float]] = defaultdict(list)
+    for r in rows:
+        try:
+            phase = int(r.get("phase", "0"))
+            ep = int(r.get("episode", "0"))
+            rew = float(r.get("total_reward", "nan"))
+        except Exception:
+            continue
+        bucket[(phase, ep)].append(rew)
+
+    keys = sorted(bucket.keys())
+    xs, ys = [], []
+    g = 0
+    last_phase = None
+    for (phase, ep) in keys:
+        if last_phase is None:
+            last_phase = phase
+        if phase != last_phase:
+            last_phase = phase
+        xs.append(g)
+        ys.append(_mean(bucket[(phase, ep)]))
+        g += 1
+    return xs, ys
+
+
+def main() -> None:
+    root = Path(__file__).resolve().parent
+    results_dir = Path(os.environ.get("TWOQUARKS_RESULTS_DIR", (root / "results").as_posix()))
+    graphics_dir = Path(os.environ.get("TWOQUARKS_GRAPHICS_DIR", (root / "graphics").as_posix()))
+    graphics_dir.mkdir(parents=True, exist_ok=True)
+
+    down_csv = results_dir / "down_paradox_tabular_results.csv"
+    anti_csv = results_dir / "antidown_corrupted_valley_tabular.csv"
+
+    if not down_csv.exists() or not anti_csv.exists():
+        print(f"[dual] missing CSVs. down={down_csv.exists()} anti={anti_csv.exists()}. Skipping.")
+        return
+
+    down_rows = _read_csv(down_csv)
+    anti_rows = _read_csv(anti_csv)
+    if not down_rows or not anti_rows:
+        print("[dual] one CSV is empty. Skipping.")
+        return
+
+    x1, y1 = _down_series(down_rows)
+    x2, y2 = _antidown_series(anti_rows)
+
+    plt.figure(figsize=(10, 5))
+    plt.plot(x1, y1, label="Down (mean across agents)")
+    plt.plot(x2, y2, label="AntiDown (mean across agents)")
+    plt.xlabel("Global episode (phase-concatenated)")
+    plt.ylabel("Episode return")
+    plt.title("Down vs AntiDown — Tabular returns (mean across agents)")
+    plt.legend()
+    out = graphics_dir / "dual_down_antidown_episode_return.png"
+    plt.tight_layout()
+    plt.savefig(out, dpi=160)
+    print(f"[dual] saved {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Down/results/antidown_corrupted_valley_tabular.csv b/Down/results/antidown_corrupted_valley_tabular.csv
new file mode 100644
index 0000000000000000000000000000000000000000..427a47f0462e24b4a744caf9aac7c4005327cf55
--- /dev/null
+++ b/Down/results/antidown_corrupted_valley_tabular.csv
@@ -0,0 +1,2401 @@
+phase,phase_tag,episode,agent,total_reward,valley_visits
+1,phase1,0,EpsGreedy,-0.5000000000000002,0
+1,phase1,1,EpsGreedy,35.49999999999999,18
+1,phase1,2,EpsGreedy,27.499999999999982,14
+1,phase1,3,EpsGreedy,0.7999999999999999,0
+1,phase1,4,EpsGreedy,85.49999999999996,43
+1,phase1,5,EpsGreedy,87.49999999999996,44
+1,phase1,6,EpsGreedy,79.49999999999999,40
+1,phase1,7,EpsGreedy,87.49999999999996,44
+1,phase1,8,EpsGreedy,87.49999999999996,44
+1,phase1,9,EpsGreedy,81.49999999999997,41
+1,phase1,10,EpsGreedy,91.49999999999996,46
+1,phase1,11,EpsGreedy,93.49999999999994,47
+1,phase1,12,EpsGreedy,81.49999999999996,41
+1,phase1,13,EpsGreedy,91.49999999999994,46
+1,phase1,14,EpsGreedy,91.49999999999996,46
+1,phase1,15,EpsGreedy,91.49999999999996,46
+1,phase1,16,EpsGreedy,91.49999999999994,46
+1,phase1,17,EpsGreedy,81.49999999999996,41
+1,phase1,18,EpsGreedy,89.49999999999996,45
+1,phase1,19,EpsGreedy,83.49999999999997,42
+1,phase1,20,EpsGreedy,81.49999999999997,41
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+1,phase1,24,EpsGreedy,65.50000000000004,33
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+1,phase1,26,EpsGreedy,85.49999999999996,43
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+1,phase1,24,Softmax,91.49999999999996,46
+1,phase1,25,Softmax,89.49999999999996,45
+1,phase1,26,Softmax,87.49999999999996,44
+1,phase1,27,Softmax,91.49999999999996,46
+1,phase1,28,Softmax,23.499999999999996,12
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+1,phase1,30,Softmax,91.49999999999996,46
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diff --git a/Down/results/down_paradox_tabular_results.csv b/Down/results/down_paradox_tabular_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5efdeb1d1448e91c4e9f1308d49e86055be464f8
--- /dev/null
+++ b/Down/results/down_paradox_tabular_results.csv
@@ -0,0 +1,2401 @@
+global_episode,phase,episode,env_seed,agent,episode_reward,failure_mode,rho_state
+0,1,0,960941037,HFLevo,1.3,neutral,nan
+0,1,0,960941037,LevoParadoxIsomer,0.0,neutral,0.5
+1,1,1,2135581874,HFLevo,1.3,neutral,nan
+1,1,1,2135581874,LevoParadoxIsomer,0.0,neutral,0.5
+2,1,2,2131554306,HFLevo,1.3,neutral,nan
+2,1,2,2131554306,LevoParadoxIsomer,0.0,neutral,0.5
+3,1,3,820359671,HFLevo,-2.0,overconfident,nan
+3,1,3,820359671,LevoParadoxIsomer,-2.0,overconfident,0.5
+4,1,4,2047814545,HFLevo,1.3,neutral,nan
+4,1,4,2047814545,LevoParadoxIsomer,1.3,neutral,0.5
+5,1,5,1776286830,HFLevo,1.2,neutral,nan
+5,1,5,1776286830,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+6,1,6,1373214917,HFLevo,-2.0,overconfident,nan
+6,1,6,1373214917,LevoParadoxIsomer,-2.0,overconfident,0.5
+7,1,7,1797992013,HFLevo,1.3,neutral,nan
+7,1,7,1797992013,LevoParadoxIsomer,0.0,neutral,0.5
+8,1,8,1645535739,HFLevo,1.2,neutral,nan
+8,1,8,1645535739,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+9,1,9,2095533891,HFLevo,1.3,neutral,nan
+9,1,9,2095533891,LevoParadoxIsomer,1.3,neutral,0.5
+10,1,10,829401385,HFLevo,1.3,neutral,nan
+10,1,10,829401385,LevoParadoxIsomer,1.3,neutral,0.5
+11,1,11,165839486,HFLevo,1.3,neutral,nan
+11,1,11,165839486,LevoParadoxIsomer,1.3,neutral,0.5
+12,1,12,976680886,HFLevo,1.2,neutral,nan
+12,1,12,976680886,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+13,1,13,681732448,HFLevo,0.0,neutral,nan
+13,1,13,681732448,LevoParadoxIsomer,0.0,neutral,0.5
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+14,1,14,1046126860,LevoParadoxIsomer,0.0,neutral,0.5
+15,1,15,1974730198,HFLevo,0.0,neutral,nan
+15,1,15,1974730198,LevoParadoxIsomer,1.3,neutral,0.5
+16,1,16,2073558604,HFLevo,0.0,neutral,nan
+16,1,16,2073558604,LevoParadoxIsomer,0.0,neutral,0.5
+17,1,17,1451418837,HFLevo,0.0,neutral,nan
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+18,1,18,1365011226,HFLevo,1.3,neutral,nan
+18,1,18,1365011226,LevoParadoxIsomer,1.3,neutral,0.5
+19,1,19,613804374,HFLevo,1.3,neutral,nan
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+20,1,20,32280267,LevoParadoxIsomer,0.0,neutral,0.5
+21,1,21,836537275,HFLevo,0.0,neutral,nan
+21,1,21,836537275,LevoParadoxIsomer,0.0,neutral,0.5
+22,1,22,975647501,HFLevo,0.0,neutral,nan
+22,1,22,975647501,LevoParadoxIsomer,-0.8,overcautious,0.5
+23,1,23,494392738,HFLevo,1.3,neutral,nan
+23,1,23,494392738,LevoParadoxIsomer,0.0,neutral,0.5
+24,1,24,1706901824,HFLevo,0.0,neutral,nan
+24,1,24,1706901824,LevoParadoxIsomer,0.0,neutral,0.5
+25,1,25,358546340,HFLevo,0.0,neutral,nan
+25,1,25,358546340,LevoParadoxIsomer,1.3,neutral,0.5
+26,1,26,1030268022,HFLevo,-2.0,overconfident,nan
+26,1,26,1030268022,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+27,1,27,329502221,HFLevo,0.0,neutral,nan
+27,1,27,329502221,LevoParadoxIsomer,0.0,neutral,0.5
+28,1,28,746578914,HFLevo,1.3,neutral,nan
+28,1,28,746578914,LevoParadoxIsomer,0.0,neutral,0.5
+29,1,29,2091845998,HFLevo,1.3,neutral,nan
+29,1,29,2091845998,LevoParadoxIsomer,1.3,neutral,0.5
+30,1,30,1436029081,HFLevo,0.0,neutral,nan
+30,1,30,1436029081,LevoParadoxIsomer,1.3,neutral,0.5
+31,1,31,910309404,HFLevo,0.0,neutral,nan
+31,1,31,910309404,LevoParadoxIsomer,1.3,neutral,0.5
+32,1,32,472048561,HFLevo,1.3,neutral,nan
+32,1,32,472048561,LevoParadoxIsomer,0.0,neutral,0.5
+33,1,33,135600480,HFLevo,0.0,neutral,nan
+33,1,33,135600480,LevoParadoxIsomer,0.0,neutral,0.5
+34,1,34,1892894588,HFLevo,0.0,neutral,nan
+34,1,34,1892894588,LevoParadoxIsomer,1.3,neutral,0.5
+35,1,35,1055673936,HFLevo,1.2,neutral,nan
+35,1,35,1055673936,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+36,1,36,1340667110,HFLevo,0.0,neutral,nan
+36,1,36,1340667110,LevoParadoxIsomer,0.0,neutral,0.5
+37,1,37,619212679,HFLevo,-2.0,overconfident,nan
+37,1,37,619212679,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+38,1,38,120545107,HFLevo,1.3,neutral,nan
+38,1,38,120545107,LevoParadoxIsomer,1.3,neutral,0.5
+39,1,39,454442609,HFLevo,2.5,neutral,nan
+39,1,39,454442609,LevoParadoxIsomer,2.5,neutral,0.5
+40,1,40,295287540,HFLevo,0.0,neutral,nan
+40,1,40,295287540,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+41,1,41,445457645,HFLevo,1.3,neutral,nan
+41,1,41,445457645,LevoParadoxIsomer,1.3,neutral,0.5
+42,1,42,307622279,HFLevo,1.3,neutral,nan
+42,1,42,307622279,LevoParadoxIsomer,1.3,neutral,0.5
+43,1,43,1243437117,HFLevo,0.0,neutral,nan
+43,1,43,1243437117,LevoParadoxIsomer,0.0,neutral,0.5
+44,1,44,244133596,HFLevo,0.0,neutral,nan
+44,1,44,244133596,LevoParadoxIsomer,1.3,neutral,0.5
+45,1,45,1268841274,HFLevo,0.0,neutral,nan
+45,1,45,1268841274,LevoParadoxIsomer,0.0,neutral,0.5
+46,1,46,1225761364,HFLevo,1.3,neutral,nan
+46,1,46,1225761364,LevoParadoxIsomer,0.0,neutral,0.5
+47,1,47,1907103971,HFLevo,1.3,neutral,nan
+47,1,47,1907103971,LevoParadoxIsomer,1.3,neutral,0.5
+48,1,48,1034799201,HFLevo,0.0,neutral,nan
+48,1,48,1034799201,LevoParadoxIsomer,0.0,neutral,0.5
+49,1,49,235864475,HFLevo,-2.0,overconfident,nan
+49,1,49,235864475,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+50,1,50,691379092,HFLevo,-2.0,overconfident,nan
+50,1,50,691379092,LevoParadoxIsomer,1.2,neutral,0.5
+51,1,51,1065800727,HFLevo,2.5,neutral,nan
+51,1,51,1065800727,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+52,1,52,655184241,HFLevo,-2.0,overconfident,nan
+52,1,52,655184241,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+53,1,53,623054291,HFLevo,2.5,neutral,nan
+53,1,53,623054291,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+54,1,54,540036626,HFLevo,-0.8,overcautious,nan
+54,1,54,540036626,LevoParadoxIsomer,0.0,neutral,0.5
+55,1,55,1043358048,HFLevo,1.2,neutral,nan
+55,1,55,1043358048,LevoParadoxIsomer,1.2,neutral,0.5
+56,1,56,1932965406,HFLevo,1.3,neutral,nan
+56,1,56,1932965406,LevoParadoxIsomer,0.0,neutral,0.5
+57,1,57,2036930070,HFLevo,1.3,neutral,nan
+57,1,57,2036930070,LevoParadoxIsomer,1.3,neutral,0.5
+58,1,58,1526743940,HFLevo,1.3,neutral,nan
+58,1,58,1526743940,LevoParadoxIsomer,0.0,neutral,0.5
+59,1,59,1989662969,HFLevo,0.0,neutral,nan
+59,1,59,1989662969,LevoParadoxIsomer,-0.8,overcautious,0.5
+60,1,60,2139514329,HFLevo,0.0,neutral,nan
+60,1,60,2139514329,LevoParadoxIsomer,0.0,neutral,0.5
+61,1,61,1632303898,HFLevo,0.0,neutral,nan
+61,1,61,1632303898,LevoParadoxIsomer,1.3,neutral,0.5
+62,1,62,1850997508,HFLevo,1.3,neutral,nan
+62,1,62,1850997508,LevoParadoxIsomer,0.0,neutral,0.5
+63,1,63,1679388031,HFLevo,1.3,neutral,nan
+63,1,63,1679388031,LevoParadoxIsomer,0.0,neutral,0.5
+64,1,64,994656789,HFLevo,0.0,neutral,nan
+64,1,64,994656789,LevoParadoxIsomer,1.3,neutral,0.5
+65,1,65,596802772,HFLevo,0.0,neutral,nan
+65,1,65,596802772,LevoParadoxIsomer,1.3,neutral,0.5
+66,1,66,2022213408,HFLevo,-0.8,overcautious,nan
+66,1,66,2022213408,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+67,1,67,1111583775,HFLevo,0.0,neutral,nan
+67,1,67,1111583775,LevoParadoxIsomer,-2.0,overconfident,0.5
+68,1,68,243487825,HFLevo,1.3,neutral,nan
+68,1,68,243487825,LevoParadoxIsomer,0.0,neutral,0.5
+69,1,69,1727825584,HFLevo,-0.8,overcautious,nan
+69,1,69,1727825584,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+70,1,70,138221208,HFLevo,0.0,neutral,nan
+70,1,70,138221208,LevoParadoxIsomer,0.0,neutral,0.5
+71,1,71,1644995000,HFLevo,1.2,neutral,nan
+71,1,71,1644995000,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+72,1,72,609184021,HFLevo,0.0,neutral,nan
+72,1,72,609184021,LevoParadoxIsomer,0.0,neutral,0.5
+73,1,73,1231929388,HFLevo,-2.0,overconfident,nan
+73,1,73,1231929388,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+74,1,74,1344075265,HFLevo,1.3,neutral,nan
+74,1,74,1344075265,LevoParadoxIsomer,1.3,neutral,0.5
+75,1,75,1828410100,HFLevo,-2.0,overconfident,nan
+75,1,75,1828410100,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+76,1,76,2048576691,HFLevo,-2.0,overconfident,nan
+76,1,76,2048576691,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+77,1,77,1829711174,HFLevo,1.3,neutral,nan
+77,1,77,1829711174,LevoParadoxIsomer,1.3,neutral,0.5
+78,1,78,993502358,HFLevo,1.3,neutral,nan
+78,1,78,993502358,LevoParadoxIsomer,0.0,neutral,0.5
+79,1,79,796970628,HFLevo,-0.8,overcautious,nan
+79,1,79,796970628,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+80,1,80,1037953041,HFLevo,0.0,neutral,nan
+80,1,80,1037953041,LevoParadoxIsomer,1.3,neutral,0.5
+81,1,81,981620733,HFLevo,0.0,neutral,nan
+81,1,81,981620733,LevoParadoxIsomer,0.0,neutral,0.5
+82,1,82,729569317,HFLevo,0.0,neutral,nan
+82,1,82,729569317,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+83,1,83,646784317,HFLevo,-2.0,overconfident,nan
+83,1,83,646784317,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+84,1,84,740999785,HFLevo,1.3,neutral,nan
+84,1,84,740999785,LevoParadoxIsomer,0.0,neutral,0.5
+85,1,85,1740597160,HFLevo,-2.0,overconfident,nan
+85,1,85,1740597160,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+86,1,86,661906414,HFLevo,1.3,neutral,nan
+86,1,86,661906414,LevoParadoxIsomer,0.0,neutral,0.5
+87,1,87,1060388906,HFLevo,1.3,neutral,nan
+87,1,87,1060388906,LevoParadoxIsomer,0.0,neutral,0.5
+88,1,88,359430184,HFLevo,2.5,neutral,nan
+88,1,88,359430184,LevoParadoxIsomer,0.0,neutral,0.5
+89,1,89,1208987810,HFLevo,1.3,neutral,nan
+89,1,89,1208987810,LevoParadoxIsomer,1.3,neutral,0.5
+90,1,90,2009099257,HFLevo,0.0,neutral,nan
+90,1,90,2009099257,LevoParadoxIsomer,1.3,neutral,0.5
+91,1,91,1034716133,HFLevo,0.0,neutral,nan
+91,1,91,1034716133,LevoParadoxIsomer,1.3,neutral,0.5
+92,1,92,868969428,HFLevo,0.0,neutral,nan
+92,1,92,868969428,LevoParadoxIsomer,-2.0,overconfident,0.3999999761581421
+93,1,93,545077016,HFLevo,2.5,neutral,nan
+93,1,93,545077016,LevoParadoxIsomer,-0.8,overcautious,0.5
+94,1,94,618241786,HFLevo,1.3,neutral,nan
+94,1,94,618241786,LevoParadoxIsomer,0.0,neutral,0.5
+95,1,95,2112109137,HFLevo,2.5,neutral,nan
+95,1,95,2112109137,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+96,1,96,300763006,HFLevo,1.3,neutral,nan
+96,1,96,300763006,LevoParadoxIsomer,0.0,neutral,0.5
+97,1,97,1182747845,HFLevo,0.0,neutral,nan
+97,1,97,1182747845,LevoParadoxIsomer,1.3,neutral,0.5
+98,1,98,997933218,HFLevo,0.0,neutral,nan
+98,1,98,997933218,LevoParadoxIsomer,1.3,neutral,0.5
+99,1,99,2082537479,HFLevo,1.3,neutral,nan
+99,1,99,2082537479,LevoParadoxIsomer,0.0,neutral,0.5
+100,1,100,948374647,HFLevo,0.0,neutral,nan
+100,1,100,948374647,LevoParadoxIsomer,1.3,neutral,0.5
+101,1,101,484158897,HFLevo,1.3,neutral,nan
+101,1,101,484158897,LevoParadoxIsomer,0.0,neutral,0.5
+102,1,102,1023803420,HFLevo,-0.8,overcautious,nan
+102,1,102,1023803420,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+103,1,103,1933155352,HFLevo,0.0,neutral,nan
+103,1,103,1933155352,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+104,1,104,531436601,HFLevo,0.0,neutral,nan
+104,1,104,531436601,LevoParadoxIsomer,0.0,neutral,0.5
+105,1,105,693213698,HFLevo,1.3,neutral,nan
+105,1,105,693213698,LevoParadoxIsomer,0.0,neutral,0.5
+106,1,106,1411219987,HFLevo,-0.8,overcautious,nan
+106,1,106,1411219987,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+107,1,107,707739816,HFLevo,0.0,neutral,nan
+107,1,107,707739816,LevoParadoxIsomer,1.3,neutral,0.5
+108,1,108,7444596,HFLevo,0.0,neutral,nan
+108,1,108,7444596,LevoParadoxIsomer,0.0,neutral,0.5
+109,1,109,1531464733,HFLevo,0.0,neutral,nan
+109,1,109,1531464733,LevoParadoxIsomer,1.3,neutral,0.5
+110,1,110,1407225246,HFLevo,1.3,neutral,nan
+110,1,110,1407225246,LevoParadoxIsomer,0.0,neutral,0.5
+111,1,111,1617971802,HFLevo,2.5,neutral,nan
+111,1,111,1617971802,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+112,1,112,1856904126,HFLevo,0.0,neutral,nan
+112,1,112,1856904126,LevoParadoxIsomer,0.0,neutral,0.5
+113,1,113,2080438781,HFLevo,0.0,neutral,nan
+113,1,113,2080438781,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+114,1,114,92845013,HFLevo,0.0,neutral,nan
+114,1,114,92845013,LevoParadoxIsomer,1.3,neutral,0.5
+115,1,115,2051487433,HFLevo,1.3,neutral,nan
+115,1,115,2051487433,LevoParadoxIsomer,0.0,neutral,0.5
+116,1,116,1625881056,HFLevo,0.0,neutral,nan
+116,1,116,1625881056,LevoParadoxIsomer,0.0,neutral,0.5
+117,1,117,1999467027,HFLevo,0.0,neutral,nan
+117,1,117,1999467027,LevoParadoxIsomer,0.0,neutral,0.5
+118,1,118,759769092,HFLevo,-0.8,overcautious,nan
+118,1,118,759769092,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+119,1,119,1368848130,HFLevo,1.3,neutral,nan
+119,1,119,1368848130,LevoParadoxIsomer,0.0,neutral,0.5
+120,1,120,2079957582,HFLevo,0.0,neutral,nan
+120,1,120,2079957582,LevoParadoxIsomer,0.0,neutral,0.5
+121,1,121,858968924,HFLevo,0.0,neutral,nan
+121,1,121,858968924,LevoParadoxIsomer,1.3,neutral,0.5
+122,1,122,2037278180,HFLevo,1.3,neutral,nan
+122,1,122,2037278180,LevoParadoxIsomer,1.3,neutral,0.5
+123,1,123,301224287,HFLevo,-0.8,overcautious,nan
+123,1,123,301224287,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+124,1,124,595106119,HFLevo,1.3,neutral,nan
+124,1,124,595106119,LevoParadoxIsomer,1.3,neutral,0.5
+125,1,125,830628267,HFLevo,1.3,neutral,nan
+125,1,125,830628267,LevoParadoxIsomer,0.0,neutral,0.5
+126,1,126,2056643707,HFLevo,0.0,neutral,nan
+126,1,126,2056643707,LevoParadoxIsomer,0.0,neutral,0.5
+127,1,127,676826452,HFLevo,0.0,neutral,nan
+127,1,127,676826452,LevoParadoxIsomer,1.3,neutral,0.5
+128,1,128,1391976925,HFLevo,2.5,neutral,nan
+128,1,128,1391976925,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+129,1,129,1312449805,HFLevo,0.0,neutral,nan
+129,1,129,1312449805,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+130,1,130,441735078,HFLevo,0.0,neutral,nan
+130,1,130,441735078,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+131,1,131,1198114699,HFLevo,1.3,neutral,nan
+131,1,131,1198114699,LevoParadoxIsomer,0.0,neutral,0.5
+132,1,132,1177544228,HFLevo,0.0,neutral,nan
+132,1,132,1177544228,LevoParadoxIsomer,0.0,neutral,0.5
+133,1,133,235682355,HFLevo,1.3,neutral,nan
+133,1,133,235682355,LevoParadoxIsomer,1.3,neutral,0.5
+134,1,134,594730155,HFLevo,2.5,neutral,nan
+134,1,134,594730155,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+135,1,135,1031283908,HFLevo,0.0,neutral,nan
+135,1,135,1031283908,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+136,1,136,1468565562,HFLevo,1.3,neutral,nan
+136,1,136,1468565562,LevoParadoxIsomer,0.0,neutral,0.5
+137,1,137,1799515670,HFLevo,-2.0,overconfident,nan
+137,1,137,1799515670,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+138,1,138,1374703813,HFLevo,1.3,neutral,nan
+138,1,138,1374703813,LevoParadoxIsomer,1.3,neutral,0.5
+139,1,139,1412669082,HFLevo,0.0,neutral,nan
+139,1,139,1412669082,LevoParadoxIsomer,0.0,neutral,0.5
+140,1,140,342509991,HFLevo,0.0,neutral,nan
+140,1,140,342509991,LevoParadoxIsomer,0.0,neutral,0.5
+141,1,141,129641700,HFLevo,1.2,neutral,nan
+141,1,141,129641700,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+142,1,142,1979539584,HFLevo,1.2,neutral,nan
+142,1,142,1979539584,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+143,1,143,181563758,HFLevo,1.3,neutral,nan
+143,1,143,181563758,LevoParadoxIsomer,1.3,neutral,0.5
+144,1,144,2121376515,HFLevo,1.3,neutral,nan
+144,1,144,2121376515,LevoParadoxIsomer,1.3,neutral,0.5
+145,1,145,359483640,HFLevo,1.2,neutral,nan
+145,1,145,359483640,LevoParadoxIsomer,-2.0,overconfident,0.34999996423721313
+146,1,146,1009660805,HFLevo,0.0,neutral,nan
+146,1,146,1009660805,LevoParadoxIsomer,0.0,neutral,0.5
+147,1,147,1772095163,HFLevo,2.5,neutral,nan
+147,1,147,1772095163,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+148,1,148,793047113,HFLevo,1.3,neutral,nan
+148,1,148,793047113,LevoParadoxIsomer,0.0,neutral,0.5
+149,1,149,2123812620,HFLevo,1.2,neutral,nan
+149,1,149,2123812620,LevoParadoxIsomer,-2.0,overconfident,0.2999999523162842
+150,1,150,160023209,HFLevo,1.2,neutral,nan
+150,1,150,160023209,LevoParadoxIsomer,-2.0,overconfident,0.24999995529651642
+151,1,151,883833601,HFLevo,1.3,neutral,nan
+151,1,151,883833601,LevoParadoxIsomer,1.3,neutral,0.5
+152,1,152,1345236526,HFLevo,0.0,neutral,nan
+152,1,152,1345236526,LevoParadoxIsomer,0.0,neutral,0.5
+153,1,153,2123563640,HFLevo,0.0,neutral,nan
+153,1,153,2123563640,LevoParadoxIsomer,1.3,neutral,0.5
+154,1,154,1188897582,HFLevo,0.0,neutral,nan
+154,1,154,1188897582,LevoParadoxIsomer,0.0,neutral,0.5
+155,1,155,1835296655,HFLevo,1.3,neutral,nan
+155,1,155,1835296655,LevoParadoxIsomer,0.0,neutral,0.5
+156,1,156,263292300,HFLevo,0.0,neutral,nan
+156,1,156,263292300,LevoParadoxIsomer,1.3,neutral,0.5
+157,1,157,339487389,HFLevo,0.0,neutral,nan
+157,1,157,339487389,LevoParadoxIsomer,0.0,neutral,0.5
+158,1,158,640187192,HFLevo,-0.8,overcautious,nan
+158,1,158,640187192,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+159,1,159,1603617236,HFLevo,0.0,neutral,nan
+159,1,159,1603617236,LevoParadoxIsomer,0.0,neutral,0.5
+160,1,160,437479272,HFLevo,2.5,neutral,nan
+160,1,160,437479272,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+161,1,161,1297938304,HFLevo,0.0,neutral,nan
+161,1,161,1297938304,LevoParadoxIsomer,1.3,neutral,0.5
+162,1,162,1736782144,HFLevo,1.3,neutral,nan
+162,1,162,1736782144,LevoParadoxIsomer,1.3,neutral,0.5
+163,1,163,1188005741,HFLevo,0.0,neutral,nan
+163,1,163,1188005741,LevoParadoxIsomer,1.3,neutral,0.5
+164,1,164,1925962876,HFLevo,1.3,neutral,nan
+164,1,164,1925962876,LevoParadoxIsomer,0.0,neutral,0.5
+165,1,165,1419686978,HFLevo,1.3,neutral,nan
+165,1,165,1419686978,LevoParadoxIsomer,0.0,neutral,0.5
+166,1,166,113085408,HFLevo,0.0,neutral,nan
+166,1,166,113085408,LevoParadoxIsomer,1.3,neutral,0.5
+167,1,167,2107650812,HFLevo,1.3,neutral,nan
+167,1,167,2107650812,LevoParadoxIsomer,0.0,neutral,0.5
+168,1,168,1556544035,HFLevo,1.3,neutral,nan
+168,1,168,1556544035,LevoParadoxIsomer,0.0,neutral,0.5
+169,1,169,409268109,HFLevo,0.0,neutral,nan
+169,1,169,409268109,LevoParadoxIsomer,0.0,neutral,0.5
+170,1,170,1707984479,HFLevo,1.3,neutral,nan
+170,1,170,1707984479,LevoParadoxIsomer,1.3,neutral,0.5
+171,1,171,2028373425,HFLevo,0.0,neutral,nan
+171,1,171,2028373425,LevoParadoxIsomer,1.3,neutral,0.5
+172,1,172,1712063860,HFLevo,1.3,neutral,nan
+172,1,172,1712063860,LevoParadoxIsomer,0.0,neutral,0.5
+173,1,173,729831432,HFLevo,0.0,neutral,nan
+173,1,173,729831432,LevoParadoxIsomer,1.3,neutral,0.5
+174,1,174,1945712103,HFLevo,-0.8,overcautious,nan
+174,1,174,1945712103,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+175,1,175,1662118687,HFLevo,1.3,neutral,nan
+175,1,175,1662118687,LevoParadoxIsomer,0.0,neutral,0.5
+176,1,176,1263277082,HFLevo,1.3,neutral,nan
+176,1,176,1263277082,LevoParadoxIsomer,1.3,neutral,0.5
+177,1,177,600780774,HFLevo,1.3,neutral,nan
+177,1,177,600780774,LevoParadoxIsomer,0.0,neutral,0.5
+178,1,178,389237547,HFLevo,0.0,neutral,nan
+178,1,178,389237547,LevoParadoxIsomer,1.3,neutral,0.5
+179,1,179,531352528,HFLevo,1.3,neutral,nan
+179,1,179,531352528,LevoParadoxIsomer,1.3,neutral,0.5
+180,1,180,308998320,HFLevo,1.2,neutral,nan
+180,1,180,308998320,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+181,1,181,133112608,HFLevo,0.0,neutral,nan
+181,1,181,133112608,LevoParadoxIsomer,0.0,neutral,0.5
+182,1,182,1335500866,HFLevo,0.0,neutral,nan
+182,1,182,1335500866,LevoParadoxIsomer,1.3,neutral,0.5
+183,1,183,1455247303,HFLevo,2.5,neutral,nan
+183,1,183,1455247303,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+184,1,184,818574077,HFLevo,1.2,neutral,nan
+184,1,184,818574077,LevoParadoxIsomer,-2.0,overconfident,0.3999999761581421
+185,1,185,783962675,HFLevo,1.2,neutral,nan
+185,1,185,783962675,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+186,1,186,181722168,HFLevo,-0.8,overcautious,nan
+186,1,186,181722168,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+187,1,187,922465315,HFLevo,1.3,neutral,nan
+187,1,187,922465315,LevoParadoxIsomer,0.0,neutral,0.5
+188,1,188,1058682574,HFLevo,0.0,neutral,nan
+188,1,188,1058682574,LevoParadoxIsomer,0.0,neutral,0.5
+189,1,189,2126397323,HFLevo,1.3,neutral,nan
+189,1,189,2126397323,LevoParadoxIsomer,1.3,neutral,0.5
+190,1,190,23796221,HFLevo,1.2,neutral,nan
+190,1,190,23796221,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+191,1,191,677074079,HFLevo,-0.8,overcautious,nan
+191,1,191,677074079,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+192,1,192,1977263236,HFLevo,1.2,neutral,nan
+192,1,192,1977263236,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+193,1,193,1768875981,HFLevo,0.0,neutral,nan
+193,1,193,1768875981,LevoParadoxIsomer,0.0,neutral,0.5
+194,1,194,1212545188,HFLevo,2.5,neutral,nan
+194,1,194,1212545188,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+195,1,195,1186674390,HFLevo,1.3,neutral,nan
+195,1,195,1186674390,LevoParadoxIsomer,0.0,neutral,0.5
+196,1,196,1056632766,HFLevo,1.3,neutral,nan
+196,1,196,1056632766,LevoParadoxIsomer,1.3,neutral,0.5
+197,1,197,268535661,HFLevo,0.0,neutral,nan
+197,1,197,268535661,LevoParadoxIsomer,1.3,neutral,0.5
+198,1,198,888167157,HFLevo,1.2,neutral,nan
+198,1,198,888167157,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+199,1,199,530391691,HFLevo,0.0,neutral,nan
+199,1,199,530391691,LevoParadoxIsomer,1.3,neutral,0.5
+200,1,200,50594990,HFLevo,2.5,neutral,nan
+200,1,200,50594990,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+201,1,201,222395105,HFLevo,1.2,neutral,nan
+201,1,201,222395105,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+202,1,202,287284573,HFLevo,1.3,neutral,nan
+202,1,202,287284573,LevoParadoxIsomer,1.3,neutral,0.5
+203,1,203,514973304,HFLevo,0.0,neutral,nan
+203,1,203,514973304,LevoParadoxIsomer,0.0,neutral,0.5
+204,1,204,1909475802,HFLevo,2.5,neutral,nan
+204,1,204,1909475802,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+205,1,205,1879525266,HFLevo,-2.0,overconfident,nan
+205,1,205,1879525266,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+206,1,206,1420380137,HFLevo,-0.8,overcautious,nan
+206,1,206,1420380137,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+207,1,207,1737331410,HFLevo,0.0,neutral,nan
+207,1,207,1737331410,LevoParadoxIsomer,1.3,neutral,0.5
+208,1,208,1064923684,HFLevo,1.3,neutral,nan
+208,1,208,1064923684,LevoParadoxIsomer,1.3,neutral,0.5
+209,1,209,1238273317,HFLevo,0.0,neutral,nan
+209,1,209,1238273317,LevoParadoxIsomer,1.3,neutral,0.5
+210,1,210,144185798,HFLevo,0.0,neutral,nan
+210,1,210,144185798,LevoParadoxIsomer,1.3,neutral,0.5
+211,1,211,575162410,HFLevo,1.3,neutral,nan
+211,1,211,575162410,LevoParadoxIsomer,0.0,neutral,0.5
+212,1,212,707283582,HFLevo,2.5,neutral,nan
+212,1,212,707283582,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+213,1,213,1405835406,HFLevo,1.3,neutral,nan
+213,1,213,1405835406,LevoParadoxIsomer,1.3,neutral,0.5
+214,1,214,789235422,HFLevo,1.3,neutral,nan
+214,1,214,789235422,LevoParadoxIsomer,0.0,neutral,0.5
+215,1,215,1388271326,HFLevo,1.3,neutral,nan
+215,1,215,1388271326,LevoParadoxIsomer,1.3,neutral,0.5
+216,1,216,1865718892,HFLevo,0.0,neutral,nan
+216,1,216,1865718892,LevoParadoxIsomer,1.3,neutral,0.5
+217,1,217,1293915651,HFLevo,1.3,neutral,nan
+217,1,217,1293915651,LevoParadoxIsomer,1.3,neutral,0.5
+218,1,218,1428150647,HFLevo,2.5,neutral,nan
+218,1,218,1428150647,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+219,1,219,19248056,HFLevo,2.5,neutral,nan
+219,1,219,19248056,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+220,1,220,491031830,HFLevo,0.0,neutral,nan
+220,1,220,491031830,LevoParadoxIsomer,0.0,neutral,0.5
+221,1,221,636126198,HFLevo,1.3,neutral,nan
+221,1,221,636126198,LevoParadoxIsomer,0.0,neutral,0.5
+222,1,222,456728828,HFLevo,1.2,neutral,nan
+222,1,222,456728828,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+223,1,223,966697912,HFLevo,1.2,neutral,nan
+223,1,223,966697912,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+224,1,224,1166498720,HFLevo,0.0,neutral,nan
+224,1,224,1166498720,LevoParadoxIsomer,1.3,neutral,0.5
+225,1,225,1571014065,HFLevo,0.0,neutral,nan
+225,1,225,1571014065,LevoParadoxIsomer,2.5,neutral,0.550000011920929
+226,1,226,714126436,HFLevo,1.3,neutral,nan
+226,1,226,714126436,LevoParadoxIsomer,0.0,neutral,0.5
+227,1,227,1206418342,HFLevo,1.3,neutral,nan
+227,1,227,1206418342,LevoParadoxIsomer,1.3,neutral,0.5
+228,1,228,1372090007,HFLevo,1.2,neutral,nan
+228,1,228,1372090007,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+229,1,229,769009050,HFLevo,2.5,neutral,nan
+229,1,229,769009050,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+230,1,230,1353001314,HFLevo,0.0,neutral,nan
+230,1,230,1353001314,LevoParadoxIsomer,0.0,neutral,0.5
+231,1,231,1733327736,HFLevo,0.0,neutral,nan
+231,1,231,1733327736,LevoParadoxIsomer,1.3,neutral,0.5
+232,1,232,638285556,HFLevo,1.3,neutral,nan
+232,1,232,638285556,LevoParadoxIsomer,1.3,neutral,0.5
+233,1,233,988454733,HFLevo,1.3,neutral,nan
+233,1,233,988454733,LevoParadoxIsomer,0.0,neutral,0.5
+234,1,234,1459581848,HFLevo,1.3,neutral,nan
+234,1,234,1459581848,LevoParadoxIsomer,0.0,neutral,0.5
+235,1,235,1106839428,HFLevo,1.2,neutral,nan
+235,1,235,1106839428,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+236,1,236,388494700,HFLevo,0.0,neutral,nan
+236,1,236,388494700,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+237,1,237,1441804184,HFLevo,0.0,neutral,nan
+237,1,237,1441804184,LevoParadoxIsomer,0.0,neutral,0.5
+238,1,238,683008548,HFLevo,0.0,neutral,nan
+238,1,238,683008548,LevoParadoxIsomer,1.3,neutral,0.5
+239,1,239,557447257,HFLevo,1.3,neutral,nan
+239,1,239,557447257,LevoParadoxIsomer,1.3,neutral,0.5
+240,1,240,1553434256,HFLevo,1.2,neutral,nan
+240,1,240,1553434256,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+241,1,241,1196216329,HFLevo,0.0,neutral,nan
+241,1,241,1196216329,LevoParadoxIsomer,1.3,neutral,0.5
+242,1,242,205445195,HFLevo,0.0,neutral,nan
+242,1,242,205445195,LevoParadoxIsomer,1.3,neutral,0.5
+243,1,243,138987130,HFLevo,1.3,neutral,nan
+243,1,243,138987130,LevoParadoxIsomer,0.0,neutral,0.5
+244,1,244,1601279830,HFLevo,0.0,neutral,nan
+244,1,244,1601279830,LevoParadoxIsomer,0.0,neutral,0.5
+245,1,245,523497866,HFLevo,2.5,neutral,nan
+245,1,245,523497866,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+246,1,246,1681903654,HFLevo,1.3,neutral,nan
+246,1,246,1681903654,LevoParadoxIsomer,0.0,neutral,0.5
+247,1,247,1447758672,HFLevo,1.3,neutral,nan
+247,1,247,1447758672,LevoParadoxIsomer,0.0,neutral,0.5
+248,1,248,1263090791,HFLevo,1.2,neutral,nan
+248,1,248,1263090791,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+249,1,249,2069934169,HFLevo,1.3,neutral,nan
+249,1,249,2069934169,LevoParadoxIsomer,1.3,neutral,0.5
+250,1,250,1175589659,HFLevo,0.0,neutral,nan
+250,1,250,1175589659,LevoParadoxIsomer,-2.0,overconfident,0.34999996423721313
+251,1,251,792436927,HFLevo,1.3,neutral,nan
+251,1,251,792436927,LevoParadoxIsomer,1.3,neutral,0.5
+252,1,252,77793463,HFLevo,1.3,neutral,nan
+252,1,252,77793463,LevoParadoxIsomer,0.0,neutral,0.5
+253,1,253,1178447001,HFLevo,0.0,neutral,nan
+253,1,253,1178447001,LevoParadoxIsomer,1.3,neutral,0.5
+254,1,254,1867902247,HFLevo,1.3,neutral,nan
+254,1,254,1867902247,LevoParadoxIsomer,0.0,neutral,0.5
+255,1,255,1132483487,HFLevo,0.0,neutral,nan
+255,1,255,1132483487,LevoParadoxIsomer,0.0,neutral,0.5
+256,1,256,1108550737,HFLevo,0.0,neutral,nan
+256,1,256,1108550737,LevoParadoxIsomer,1.3,neutral,0.5
+257,1,257,761845810,HFLevo,0.0,neutral,nan
+257,1,257,761845810,LevoParadoxIsomer,1.3,neutral,0.5
+258,1,258,2026103214,HFLevo,2.5,neutral,nan
+258,1,258,2026103214,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+259,1,259,1320383576,HFLevo,2.5,neutral,nan
+259,1,259,1320383576,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+260,1,260,165278918,HFLevo,0.0,neutral,nan
+260,1,260,165278918,LevoParadoxIsomer,-2.0,overconfident,0.2999999523162842
+261,1,261,1264440670,HFLevo,2.5,neutral,nan
+261,1,261,1264440670,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+262,1,262,859892894,HFLevo,1.3,neutral,nan
+262,1,262,859892894,LevoParadoxIsomer,0.0,neutral,0.5
+263,1,263,411458685,HFLevo,0.0,neutral,nan
+263,1,263,411458685,LevoParadoxIsomer,1.3,neutral,0.5
+264,1,264,1931263994,HFLevo,0.0,neutral,nan
+264,1,264,1931263994,LevoParadoxIsomer,0.0,neutral,0.5
+265,1,265,213511530,HFLevo,2.5,neutral,nan
+265,1,265,213511530,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+266,1,266,1137608613,HFLevo,0.0,neutral,nan
+266,1,266,1137608613,LevoParadoxIsomer,0.0,neutral,0.44999998807907104
+267,1,267,843567272,HFLevo,0.0,neutral,nan
+267,1,267,843567272,LevoParadoxIsomer,0.0,neutral,0.5
+268,1,268,11175988,HFLevo,1.3,neutral,nan
+268,1,268,11175988,LevoParadoxIsomer,1.3,neutral,0.5
+269,1,269,687107894,HFLevo,0.0,neutral,nan
+269,1,269,687107894,LevoParadoxIsomer,0.0,neutral,0.5
+270,1,270,291918902,HFLevo,2.5,neutral,nan
+270,1,270,291918902,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+271,1,271,1087591275,HFLevo,2.5,neutral,nan
+271,1,271,1087591275,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+272,1,272,1813734633,HFLevo,2.5,neutral,nan
+272,1,272,1813734633,LevoParadoxIsomer,2.5,neutral,0.6000000238418579
+273,1,273,461816730,HFLevo,0.0,neutral,nan
+273,1,273,461816730,LevoParadoxIsomer,1.3,neutral,0.5
+274,1,274,441050625,HFLevo,1.3,neutral,nan
+274,1,274,441050625,LevoParadoxIsomer,1.3,neutral,0.5
+275,1,275,2141182885,HFLevo,1.3,neutral,nan
+275,1,275,2141182885,LevoParadoxIsomer,0.0,neutral,0.5
+276,1,276,749647573,HFLevo,0.0,neutral,nan
+276,1,276,749647573,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+277,1,277,702601389,HFLevo,0.0,neutral,nan
+277,1,277,702601389,LevoParadoxIsomer,0.0,neutral,0.5
+278,1,278,500813039,HFLevo,0.0,neutral,nan
+278,1,278,500813039,LevoParadoxIsomer,1.3,neutral,0.5
+279,1,279,1327850191,HFLevo,1.2,neutral,nan
+279,1,279,1327850191,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+280,1,280,1479353871,HFLevo,-0.8,overcautious,nan
+280,1,280,1479353871,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+281,1,281,875883029,HFLevo,2.5,neutral,nan
+281,1,281,875883029,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+282,1,282,956514865,HFLevo,-2.0,overconfident,nan
+282,1,282,956514865,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+283,1,283,140168808,HFLevo,2.5,neutral,nan
+283,1,283,140168808,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+284,1,284,2023927855,HFLevo,0.0,neutral,nan
+284,1,284,2023927855,LevoParadoxIsomer,1.3,neutral,0.5
+285,1,285,1850097089,HFLevo,0.0,neutral,nan
+285,1,285,1850097089,LevoParadoxIsomer,1.3,neutral,0.5
+286,1,286,366267895,HFLevo,0.0,neutral,nan
+286,1,286,366267895,LevoParadoxIsomer,1.3,neutral,0.5
+287,1,287,833125277,HFLevo,0.0,neutral,nan
+287,1,287,833125277,LevoParadoxIsomer,1.3,neutral,0.5
+288,1,288,1062058470,HFLevo,0.0,neutral,nan
+288,1,288,1062058470,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+289,1,289,755011055,HFLevo,1.3,neutral,nan
+289,1,289,755011055,LevoParadoxIsomer,1.3,neutral,0.5
+290,1,290,1193341725,HFLevo,1.3,neutral,nan
+290,1,290,1193341725,LevoParadoxIsomer,0.0,neutral,0.5
+291,1,291,47566427,HFLevo,0.0,neutral,nan
+291,1,291,47566427,LevoParadoxIsomer,1.3,neutral,0.5
+292,1,292,1480907230,HFLevo,0.0,neutral,nan
+292,1,292,1480907230,LevoParadoxIsomer,1.3,neutral,0.5
+293,1,293,1193747826,HFLevo,1.3,neutral,nan
+293,1,293,1193747826,LevoParadoxIsomer,1.3,neutral,0.5
+294,1,294,188056822,HFLevo,0.0,neutral,nan
+294,1,294,188056822,LevoParadoxIsomer,0.0,neutral,0.5
+295,1,295,2058188731,HFLevo,1.3,neutral,nan
+295,1,295,2058188731,LevoParadoxIsomer,0.0,neutral,0.5
+296,1,296,131903334,HFLevo,-0.8,overcautious,nan
+296,1,296,131903334,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+297,1,297,2023999375,HFLevo,0.0,neutral,nan
+297,1,297,2023999375,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+298,1,298,14284283,HFLevo,0.0,neutral,nan
+298,1,298,14284283,LevoParadoxIsomer,1.3,neutral,0.5
+299,1,299,430728515,HFLevo,0.0,neutral,nan
+299,1,299,430728515,LevoParadoxIsomer,1.3,neutral,0.5
+300,1,300,1773637227,HFLevo,1.3,neutral,nan
+300,1,300,1773637227,LevoParadoxIsomer,1.3,neutral,0.5
+301,1,301,1447643177,HFLevo,0.0,neutral,nan
+301,1,301,1447643177,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+302,1,302,980890604,HFLevo,2.5,neutral,nan
+302,1,302,980890604,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+303,1,303,1738676116,HFLevo,0.0,neutral,nan
+303,1,303,1738676116,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+304,1,304,202567723,HFLevo,0.0,neutral,nan
+304,1,304,202567723,LevoParadoxIsomer,1.3,neutral,0.5
+305,1,305,1153805088,HFLevo,0.0,neutral,nan
+305,1,305,1153805088,LevoParadoxIsomer,-2.0,overconfident,0.24999995529651642
+306,1,306,972971566,HFLevo,-2.0,overconfident,nan
+306,1,306,972971566,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+307,1,307,1472596543,HFLevo,2.5,neutral,nan
+307,1,307,1472596543,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+308,1,308,1472317967,HFLevo,1.3,neutral,nan
+308,1,308,1472317967,LevoParadoxIsomer,1.3,neutral,0.5
+309,1,309,215008918,HFLevo,0.0,neutral,nan
+309,1,309,215008918,LevoParadoxIsomer,0.0,neutral,0.5
+310,1,310,1105771947,HFLevo,1.3,neutral,nan
+310,1,310,1105771947,LevoParadoxIsomer,1.3,neutral,0.5
+311,1,311,652569339,HFLevo,1.3,neutral,nan
+311,1,311,652569339,LevoParadoxIsomer,1.3,neutral,0.5
+312,1,312,1726057777,HFLevo,2.5,neutral,nan
+312,1,312,1726057777,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+313,1,313,1485944499,HFLevo,0.0,neutral,nan
+313,1,313,1485944499,LevoParadoxIsomer,1.3,neutral,0.5
+314,1,314,294205674,HFLevo,1.3,neutral,nan
+314,1,314,294205674,LevoParadoxIsomer,1.3,neutral,0.5
+315,1,315,1016871026,HFLevo,1.2,neutral,nan
+315,1,315,1016871026,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+316,1,316,1168910589,HFLevo,1.3,neutral,nan
+316,1,316,1168910589,LevoParadoxIsomer,0.0,neutral,0.5
+317,1,317,1517292813,HFLevo,1.3,neutral,nan
+317,1,317,1517292813,LevoParadoxIsomer,0.0,neutral,0.5
+318,1,318,2030522066,HFLevo,2.5,neutral,nan
+318,1,318,2030522066,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+319,1,319,959589186,HFLevo,0.0,neutral,nan
+319,1,319,959589186,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+320,1,320,590193953,HFLevo,1.3,neutral,nan
+320,1,320,590193953,LevoParadoxIsomer,1.3,neutral,0.5
+321,1,321,675005705,HFLevo,1.3,neutral,nan
+321,1,321,675005705,LevoParadoxIsomer,1.3,neutral,0.5
+322,1,322,925232956,HFLevo,0.0,neutral,nan
+322,1,322,925232956,LevoParadoxIsomer,0.0,neutral,0.5
+323,1,323,1408472699,HFLevo,2.5,neutral,nan
+323,1,323,1408472699,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+324,1,324,859189252,HFLevo,1.3,neutral,nan
+324,1,324,859189252,LevoParadoxIsomer,0.0,neutral,0.5
+325,1,325,399323533,HFLevo,0.0,neutral,nan
+325,1,325,399323533,LevoParadoxIsomer,0.0,neutral,0.5
+326,1,326,980315658,HFLevo,-2.0,overconfident,nan
+326,1,326,980315658,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+327,1,327,123987181,HFLevo,1.2,neutral,nan
+327,1,327,123987181,LevoParadoxIsomer,-2.0,overconfident,0.44999998807907104
+328,1,328,409996997,HFLevo,1.3,neutral,nan
+328,1,328,409996997,LevoParadoxIsomer,1.3,neutral,0.5
+329,1,329,708892450,HFLevo,1.2,neutral,nan
+329,1,329,708892450,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+330,1,330,1373932302,HFLevo,0.0,neutral,nan
+330,1,330,1373932302,LevoParadoxIsomer,1.3,neutral,0.5
+331,1,331,2077742390,HFLevo,1.3,neutral,nan
+331,1,331,2077742390,LevoParadoxIsomer,1.3,neutral,0.5
+332,1,332,761108960,HFLevo,1.3,neutral,nan
+332,1,332,761108960,LevoParadoxIsomer,0.0,neutral,0.5
+333,1,333,1211512608,HFLevo,1.3,neutral,nan
+333,1,333,1211512608,LevoParadoxIsomer,0.0,neutral,0.5
+334,1,334,1884662523,HFLevo,0.0,neutral,nan
+334,1,334,1884662523,LevoParadoxIsomer,0.0,neutral,0.5
+335,1,335,2002191522,HFLevo,1.3,neutral,nan
+335,1,335,2002191522,LevoParadoxIsomer,1.3,neutral,0.5
+336,1,336,2028646946,HFLevo,0.0,neutral,nan
+336,1,336,2028646946,LevoParadoxIsomer,1.3,neutral,0.5
+337,1,337,589112024,HFLevo,1.3,neutral,nan
+337,1,337,589112024,LevoParadoxIsomer,1.3,neutral,0.5
+338,1,338,610703030,HFLevo,1.3,neutral,nan
+338,1,338,610703030,LevoParadoxIsomer,1.3,neutral,0.5
+339,1,339,972829234,HFLevo,0.0,neutral,nan
+339,1,339,972829234,LevoParadoxIsomer,1.3,neutral,0.5
+340,1,340,465154269,HFLevo,2.5,neutral,nan
+340,1,340,465154269,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+341,1,341,470901255,HFLevo,1.3,neutral,nan
+341,1,341,470901255,LevoParadoxIsomer,1.3,neutral,0.5
+342,1,342,1990213325,HFLevo,1.3,neutral,nan
+342,1,342,1990213325,LevoParadoxIsomer,0.0,neutral,0.5
+343,1,343,1646775850,HFLevo,1.3,neutral,nan
+343,1,343,1646775850,LevoParadoxIsomer,0.0,neutral,0.5
+344,1,344,1057311607,HFLevo,0.0,neutral,nan
+344,1,344,1057311607,LevoParadoxIsomer,1.3,neutral,0.5
+345,1,345,590319545,HFLevo,1.3,neutral,nan
+345,1,345,590319545,LevoParadoxIsomer,0.0,neutral,0.5
+346,1,346,516318123,HFLevo,1.3,neutral,nan
+346,1,346,516318123,LevoParadoxIsomer,0.0,neutral,0.5
+347,1,347,70890343,HFLevo,1.3,neutral,nan
+347,1,347,70890343,LevoParadoxIsomer,1.3,neutral,0.5
+348,1,348,804413128,HFLevo,2.5,neutral,nan
+348,1,348,804413128,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+349,1,349,1504695760,HFLevo,0.0,neutral,nan
+349,1,349,1504695760,LevoParadoxIsomer,1.3,neutral,0.5
+350,1,350,940669613,HFLevo,0.0,neutral,nan
+350,1,350,940669613,LevoParadoxIsomer,0.0,neutral,0.5
+351,1,351,821018278,HFLevo,1.3,neutral,nan
+351,1,351,821018278,LevoParadoxIsomer,1.3,neutral,0.5
+352,1,352,2080007758,HFLevo,2.5,neutral,nan
+352,1,352,2080007758,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+353,1,353,461373511,HFLevo,1.3,neutral,nan
+353,1,353,461373511,LevoParadoxIsomer,0.0,neutral,0.5
+354,1,354,1987269884,HFLevo,1.3,neutral,nan
+354,1,354,1987269884,LevoParadoxIsomer,0.0,neutral,0.5
+355,1,355,885162807,HFLevo,1.3,neutral,nan
+355,1,355,885162807,LevoParadoxIsomer,0.0,neutral,0.5
+356,1,356,779768070,HFLevo,1.3,neutral,nan
+356,1,356,779768070,LevoParadoxIsomer,1.3,neutral,0.5
+357,1,357,966063187,HFLevo,1.2,neutral,nan
+357,1,357,966063187,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+358,1,358,1359588046,HFLevo,1.3,neutral,nan
+358,1,358,1359588046,LevoParadoxIsomer,1.3,neutral,0.5
+359,1,359,393633121,HFLevo,1.3,neutral,nan
+359,1,359,393633121,LevoParadoxIsomer,0.0,neutral,0.5
+360,1,360,991070115,HFLevo,1.3,neutral,nan
+360,1,360,991070115,LevoParadoxIsomer,1.3,neutral,0.5
+361,1,361,1695833246,HFLevo,1.3,neutral,nan
+361,1,361,1695833246,LevoParadoxIsomer,1.3,neutral,0.5
+362,1,362,259218709,HFLevo,1.2,neutral,nan
+362,1,362,259218709,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+363,1,363,1252523270,HFLevo,1.2,neutral,nan
+363,1,363,1252523270,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+364,1,364,618943155,HFLevo,1.3,neutral,nan
+364,1,364,618943155,LevoParadoxIsomer,1.3,neutral,0.5
+365,1,365,1149281050,HFLevo,1.3,neutral,nan
+365,1,365,1149281050,LevoParadoxIsomer,1.3,neutral,0.5
+366,1,366,605383710,HFLevo,1.3,neutral,nan
+366,1,366,605383710,LevoParadoxIsomer,1.3,neutral,0.5
+367,1,367,1156676417,HFLevo,1.2,neutral,nan
+367,1,367,1156676417,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+368,1,368,988215448,HFLevo,1.3,neutral,nan
+368,1,368,988215448,LevoParadoxIsomer,1.3,neutral,0.5
+369,1,369,1630817173,HFLevo,2.5,neutral,nan
+369,1,369,1630817173,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+370,1,370,1326405127,HFLevo,1.3,neutral,nan
+370,1,370,1326405127,LevoParadoxIsomer,1.3,neutral,0.5
+371,1,371,279363521,HFLevo,1.3,neutral,nan
+371,1,371,279363521,LevoParadoxIsomer,0.0,neutral,0.5
+372,1,372,673386525,HFLevo,1.3,neutral,nan
+372,1,372,673386525,LevoParadoxIsomer,1.3,neutral,0.5
+373,1,373,1485190259,HFLevo,0.0,neutral,nan
+373,1,373,1485190259,LevoParadoxIsomer,0.0,neutral,0.5
+374,1,374,757249346,HFLevo,1.3,neutral,nan
+374,1,374,757249346,LevoParadoxIsomer,1.3,neutral,0.5
+375,1,375,2139173341,HFLevo,2.5,neutral,nan
+375,1,375,2139173341,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+376,1,376,1407830714,HFLevo,1.3,neutral,nan
+376,1,376,1407830714,LevoParadoxIsomer,1.3,neutral,0.5
+377,1,377,1328293493,HFLevo,1.2,neutral,nan
+377,1,377,1328293493,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+378,1,378,1561225140,HFLevo,1.3,neutral,nan
+378,1,378,1561225140,LevoParadoxIsomer,1.3,neutral,0.5
+379,1,379,2054053838,HFLevo,2.5,neutral,nan
+379,1,379,2054053838,LevoParadoxIsomer,2.5,neutral,0.7000000476837158
+380,1,380,1538665569,HFLevo,1.2,neutral,nan
+380,1,380,1538665569,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+381,1,381,578245576,HFLevo,1.3,neutral,nan
+381,1,381,578245576,LevoParadoxIsomer,0.0,neutral,0.5
+382,1,382,1001750142,HFLevo,1.3,neutral,nan
+382,1,382,1001750142,LevoParadoxIsomer,1.3,neutral,0.5
+383,1,383,247349919,HFLevo,1.3,neutral,nan
+383,1,383,247349919,LevoParadoxIsomer,0.0,neutral,0.5
+384,1,384,1913641746,HFLevo,1.3,neutral,nan
+384,1,384,1913641746,LevoParadoxIsomer,0.0,neutral,0.5
+385,1,385,191896624,HFLevo,2.5,neutral,nan
+385,1,385,191896624,LevoParadoxIsomer,2.5,neutral,0.7500000596046448
+386,1,386,358450787,HFLevo,0.0,neutral,nan
+386,1,386,358450787,LevoParadoxIsomer,1.3,neutral,0.5
+387,1,387,1304006934,HFLevo,0.0,neutral,nan
+387,1,387,1304006934,LevoParadoxIsomer,1.3,neutral,0.5
+388,1,388,854476003,HFLevo,0.0,neutral,nan
+388,1,388,854476003,LevoParadoxIsomer,0.0,neutral,0.5
+389,1,389,718810870,HFLevo,1.3,neutral,nan
+389,1,389,718810870,LevoParadoxIsomer,1.3,neutral,0.5
+390,1,390,2087848673,HFLevo,1.3,neutral,nan
+390,1,390,2087848673,LevoParadoxIsomer,1.3,neutral,0.5
+391,1,391,565983639,HFLevo,0.0,neutral,nan
+391,1,391,565983639,LevoParadoxIsomer,2.5,neutral,0.6500000357627869
+392,1,392,406083697,HFLevo,1.3,neutral,nan
+392,1,392,406083697,LevoParadoxIsomer,0.0,neutral,0.5
+393,1,393,1812367355,HFLevo,1.3,neutral,nan
+393,1,393,1812367355,LevoParadoxIsomer,0.0,neutral,0.5
+394,1,394,1054994518,HFLevo,0.0,neutral,nan
+394,1,394,1054994518,LevoParadoxIsomer,1.3,neutral,0.5
+395,1,395,235136936,HFLevo,1.3,neutral,nan
+395,1,395,235136936,LevoParadoxIsomer,0.0,neutral,0.5
+396,1,396,111732250,HFLevo,0.0,neutral,nan
+396,1,396,111732250,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+397,1,397,289408596,HFLevo,0.0,neutral,nan
+397,1,397,289408596,LevoParadoxIsomer,1.3,neutral,0.5
+398,1,398,1197540953,HFLevo,1.3,neutral,nan
+398,1,398,1197540953,LevoParadoxIsomer,0.0,neutral,0.5
+399,1,399,34196977,HFLevo,0.0,neutral,nan
+399,1,399,34196977,LevoParadoxIsomer,1.3,neutral,0.5
+400,2,0,2034269964,HFLevo,0.0,neutral,nan
+400,2,0,2034269964,LevoParadoxIsomer,0.8,neutral,0.5
+401,2,1,912146831,HFLevo,2.0,neutral,nan
+401,2,1,912146831,LevoParadoxIsomer,2.0,neutral,0.5
+402,2,2,144856919,HFLevo,1.2,neutral,nan
+402,2,2,144856919,LevoParadoxIsomer,-3.0,overconfident,0.5
+403,2,3,685506823,HFLevo,-3.0,overconfident,nan
+403,2,3,685506823,LevoParadoxIsomer,1.2,neutral,0.5
+404,2,4,1939181862,HFLevo,0.0,neutral,nan
+404,2,4,1939181862,LevoParadoxIsomer,0.0,neutral,0.5
+405,2,5,1502803201,HFLevo,-0.8,overcautious,nan
+405,2,5,1502803201,LevoParadoxIsomer,-0.8,overcautious,0.5
+406,2,6,1504803878,HFLevo,-3.0,overconfident,nan
+406,2,6,1504803878,LevoParadoxIsomer,-3.0,overconfident,0.5
+407,2,7,67883367,HFLevo,-0.8,overcautious,nan
+407,2,7,67883367,LevoParadoxIsomer,-0.8,overcautious,0.5
+408,2,8,235174993,HFLevo,0.0,neutral,nan
+408,2,8,235174993,LevoParadoxIsomer,0.0,neutral,0.5
+409,2,9,1767017071,HFLevo,-0.8,overcautious,nan
+409,2,9,1767017071,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+410,2,10,1195637117,HFLevo,1.2,neutral,nan
+410,2,10,1195637117,LevoParadoxIsomer,-3.0,overconfident,0.5
+411,2,11,2140388521,HFLevo,0.0,neutral,nan
+411,2,11,2140388521,LevoParadoxIsomer,0.8,neutral,0.5
+412,2,12,285128217,HFLevo,-3.0,overconfident,nan
+412,2,12,285128217,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+413,2,13,1297214282,HFLevo,0.8,neutral,nan
+413,2,13,1297214282,LevoParadoxIsomer,0.0,neutral,0.5
+414,2,14,2128926662,HFLevo,0.8,neutral,nan
+414,2,14,2128926662,LevoParadoxIsomer,0.8,neutral,0.5
+415,2,15,1227615478,HFLevo,0.8,neutral,nan
+415,2,15,1227615478,LevoParadoxIsomer,0.0,neutral,0.5
+416,2,16,562820587,HFLevo,0.0,neutral,nan
+416,2,16,562820587,LevoParadoxIsomer,1.2,neutral,0.44999998807907104
+417,2,17,1938965903,HFLevo,0.0,neutral,nan
+417,2,17,1938965903,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+418,2,18,1938899583,HFLevo,0.0,neutral,nan
+418,2,18,1938899583,LevoParadoxIsomer,0.0,neutral,0.5
+419,2,19,146416322,HFLevo,2.0,neutral,nan
+419,2,19,146416322,LevoParadoxIsomer,0.0,neutral,0.550000011920929
+420,2,20,2034761172,HFLevo,0.8,neutral,nan
+420,2,20,2034761172,LevoParadoxIsomer,0.0,neutral,0.5
+421,2,21,1531184118,HFLevo,0.0,neutral,nan
+421,2,21,1531184118,LevoParadoxIsomer,0.0,neutral,0.5
+422,2,22,1624823478,HFLevo,1.2,neutral,nan
+422,2,22,1624823478,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+423,2,23,1973982599,HFLevo,-0.8,overcautious,nan
+423,2,23,1973982599,LevoParadoxIsomer,2.0,neutral,0.550000011920929
+424,2,24,1433121745,HFLevo,0.8,neutral,nan
+424,2,24,1433121745,LevoParadoxIsomer,0.8,neutral,0.5
+425,2,25,269942302,HFLevo,0.0,neutral,nan
+425,2,25,269942302,LevoParadoxIsomer,2.0,neutral,0.550000011920929
+426,2,26,662577618,HFLevo,1.2,neutral,nan
+426,2,26,662577618,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+427,2,27,1680792863,HFLevo,0.0,neutral,nan
+427,2,27,1680792863,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+428,2,28,608196857,HFLevo,0.0,neutral,nan
+428,2,28,608196857,LevoParadoxIsomer,0.8,neutral,0.5
+429,2,29,750797946,HFLevo,0.8,neutral,nan
+429,2,29,750797946,LevoParadoxIsomer,0.8,neutral,0.5
+430,2,30,1703067462,HFLevo,0.8,neutral,nan
+430,2,30,1703067462,LevoParadoxIsomer,0.0,neutral,0.5
+431,2,31,354277540,HFLevo,0.0,neutral,nan
+431,2,31,354277540,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+432,2,32,516686757,HFLevo,0.0,neutral,nan
+432,2,32,516686757,LevoParadoxIsomer,0.0,neutral,0.5
+433,2,33,145581260,HFLevo,0.8,neutral,nan
+433,2,33,145581260,LevoParadoxIsomer,0.8,neutral,0.5
+434,2,34,1801241574,HFLevo,2.0,neutral,nan
+434,2,34,1801241574,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+435,2,35,2100309440,HFLevo,2.0,neutral,nan
+435,2,35,2100309440,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+436,2,36,1637824222,HFLevo,0.8,neutral,nan
+436,2,36,1637824222,LevoParadoxIsomer,0.8,neutral,0.5
+437,2,37,1781578254,HFLevo,0.0,neutral,nan
+437,2,37,1781578254,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+438,2,38,774535340,HFLevo,0.8,neutral,nan
+438,2,38,774535340,LevoParadoxIsomer,0.8,neutral,0.5
+439,2,39,1592536280,HFLevo,0.8,neutral,nan
+439,2,39,1592536280,LevoParadoxIsomer,0.8,neutral,0.5
+440,2,40,803715272,HFLevo,0.8,neutral,nan
+440,2,40,803715272,LevoParadoxIsomer,0.0,neutral,0.5
+441,2,41,1295445709,HFLevo,-0.8,overcautious,nan
+441,2,41,1295445709,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+442,2,42,580455628,HFLevo,0.0,neutral,nan
+442,2,42,580455628,LevoParadoxIsomer,0.8,neutral,0.5
+443,2,43,608571353,HFLevo,0.0,neutral,nan
+443,2,43,608571353,LevoParadoxIsomer,0.0,neutral,0.5
+444,2,44,220247617,HFLevo,0.0,neutral,nan
+444,2,44,220247617,LevoParadoxIsomer,0.8,neutral,0.5
+445,2,45,1007502304,HFLevo,0.8,neutral,nan
+445,2,45,1007502304,LevoParadoxIsomer,0.8,neutral,0.5
+446,2,46,1485027697,HFLevo,0.0,neutral,nan
+446,2,46,1485027697,LevoParadoxIsomer,1.2,neutral,0.3999999761581421
+447,2,47,308424123,HFLevo,0.8,neutral,nan
+447,2,47,308424123,LevoParadoxIsomer,0.8,neutral,0.5
+448,2,48,1831539103,HFLevo,0.0,neutral,nan
+448,2,48,1831539103,LevoParadoxIsomer,0.0,neutral,0.5
+449,2,49,96205481,HFLevo,0.8,neutral,nan
+449,2,49,96205481,LevoParadoxIsomer,0.0,neutral,0.5
+450,2,50,572983894,HFLevo,0.0,neutral,nan
+450,2,50,572983894,LevoParadoxIsomer,0.8,neutral,0.5
+451,2,51,1559559556,HFLevo,0.8,neutral,nan
+451,2,51,1559559556,LevoParadoxIsomer,0.0,neutral,0.5
+452,2,52,289588363,HFLevo,0.8,neutral,nan
+452,2,52,289588363,LevoParadoxIsomer,0.8,neutral,0.5
+453,2,53,1021080331,HFLevo,0.8,neutral,nan
+453,2,53,1021080331,LevoParadoxIsomer,0.0,neutral,0.5
+454,2,54,43845046,HFLevo,0.8,neutral,nan
+454,2,54,43845046,LevoParadoxIsomer,0.8,neutral,0.5
+455,2,55,1969391451,HFLevo,0.0,neutral,nan
+455,2,55,1969391451,LevoParadoxIsomer,0.0,neutral,0.5
+456,2,56,112141255,HFLevo,0.8,neutral,nan
+456,2,56,112141255,LevoParadoxIsomer,0.8,neutral,0.5
+457,2,57,407044235,HFLevo,0.0,neutral,nan
+457,2,57,407044235,LevoParadoxIsomer,0.0,neutral,0.5
+458,2,58,1413331621,HFLevo,0.8,neutral,nan
+458,2,58,1413331621,LevoParadoxIsomer,0.8,neutral,0.5
+459,2,59,1124438664,HFLevo,2.0,neutral,nan
+459,2,59,1124438664,LevoParadoxIsomer,-0.8,overcautious,0.5
+460,2,60,1823383091,HFLevo,0.0,neutral,nan
+460,2,60,1823383091,LevoParadoxIsomer,0.8,neutral,0.5
+461,2,61,1452420947,HFLevo,1.2,neutral,nan
+461,2,61,1452420947,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+462,2,62,2031250630,HFLevo,1.2,neutral,nan
+462,2,62,2031250630,LevoParadoxIsomer,-3.0,overconfident,0.44999998807907104
+463,2,63,788674418,HFLevo,0.8,neutral,nan
+463,2,63,788674418,LevoParadoxIsomer,0.0,neutral,0.5
+464,2,64,1437390327,HFLevo,0.0,neutral,nan
+464,2,64,1437390327,LevoParadoxIsomer,0.8,neutral,0.5
+465,2,65,879262169,HFLevo,2.0,neutral,nan
+465,2,65,879262169,LevoParadoxIsomer,-0.8,overcautious,0.550000011920929
+466,2,66,958652521,HFLevo,0.8,neutral,nan
+466,2,66,958652521,LevoParadoxIsomer,0.0,neutral,0.5
+467,2,67,2142602411,HFLevo,0.8,neutral,nan
+467,2,67,2142602411,LevoParadoxIsomer,0.0,neutral,0.5
+468,2,68,59947504,HFLevo,0.8,neutral,nan
+468,2,68,59947504,LevoParadoxIsomer,0.0,neutral,0.5
+469,2,69,384107305,HFLevo,0.0,neutral,nan
+469,2,69,384107305,LevoParadoxIsomer,0.8,neutral,0.5
+470,2,70,1830507375,HFLevo,0.8,neutral,nan
+470,2,70,1830507375,LevoParadoxIsomer,0.8,neutral,0.5
+471,2,71,571519169,HFLevo,0.8,neutral,nan
+471,2,71,571519169,LevoParadoxIsomer,0.0,neutral,0.5
+472,2,72,1626492832,HFLevo,0.8,neutral,nan
+472,2,72,1626492832,LevoParadoxIsomer,0.8,neutral,0.5
+473,2,73,1386853771,HFLevo,0.0,neutral,nan
+473,2,73,1386853771,LevoParadoxIsomer,0.8,neutral,0.5
+474,2,74,1706685627,HFLevo,2.0,neutral,nan
+474,2,74,1706685627,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+475,2,75,1611897310,HFLevo,2.0,neutral,nan
+475,2,75,1611897310,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+476,2,76,1751003019,HFLevo,0.0,neutral,nan
+476,2,76,1751003019,LevoParadoxIsomer,0.8,neutral,0.5
+477,2,77,1422082962,HFLevo,0.0,neutral,nan
+477,2,77,1422082962,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+478,2,78,1883691922,HFLevo,0.0,neutral,nan
+478,2,78,1883691922,LevoParadoxIsomer,0.8,neutral,0.5
+479,2,79,1013721795,HFLevo,0.8,neutral,nan
+479,2,79,1013721795,LevoParadoxIsomer,0.8,neutral,0.5
+480,2,80,820766304,HFLevo,0.8,neutral,nan
+480,2,80,820766304,LevoParadoxIsomer,0.8,neutral,0.5
+481,2,81,292577686,HFLevo,0.8,neutral,nan
+481,2,81,292577686,LevoParadoxIsomer,0.8,neutral,0.5
+482,2,82,1774181236,HFLevo,0.0,neutral,nan
+482,2,82,1774181236,LevoParadoxIsomer,0.0,neutral,0.5
+483,2,83,16477750,HFLevo,0.8,neutral,nan
+483,2,83,16477750,LevoParadoxIsomer,0.0,neutral,0.5
+484,2,84,270709347,HFLevo,0.8,neutral,nan
+484,2,84,270709347,LevoParadoxIsomer,0.0,neutral,0.5
+485,2,85,117649210,HFLevo,0.0,neutral,nan
+485,2,85,117649210,LevoParadoxIsomer,0.8,neutral,0.5
+486,2,86,1315442558,HFLevo,0.0,neutral,nan
+486,2,86,1315442558,LevoParadoxIsomer,0.0,neutral,0.5
+487,2,87,2057676732,HFLevo,0.0,neutral,nan
+487,2,87,2057676732,LevoParadoxIsomer,0.0,neutral,0.5
+488,2,88,211459733,HFLevo,0.8,neutral,nan
+488,2,88,211459733,LevoParadoxIsomer,0.8,neutral,0.5
+489,2,89,91146202,HFLevo,0.8,neutral,nan
+489,2,89,91146202,LevoParadoxIsomer,0.0,neutral,0.5
+490,2,90,588068454,HFLevo,0.8,neutral,nan
+490,2,90,588068454,LevoParadoxIsomer,0.0,neutral,0.5
+491,2,91,467526601,HFLevo,2.0,neutral,nan
+491,2,91,467526601,LevoParadoxIsomer,0.0,neutral,0.6000000238418579
+492,2,92,335299668,HFLevo,0.0,neutral,nan
+492,2,92,335299668,LevoParadoxIsomer,0.0,neutral,0.5
+493,2,93,779362037,HFLevo,0.8,neutral,nan
+493,2,93,779362037,LevoParadoxIsomer,0.0,neutral,0.5
+494,2,94,660339976,HFLevo,2.0,neutral,nan
+494,2,94,660339976,LevoParadoxIsomer,2.0,neutral,0.6000000238418579
+495,2,95,637136818,HFLevo,1.2,neutral,nan
+495,2,95,637136818,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+496,2,96,1999866855,HFLevo,0.8,neutral,nan
+496,2,96,1999866855,LevoParadoxIsomer,0.0,neutral,0.5
+497,2,97,139896496,HFLevo,0.8,neutral,nan
+497,2,97,139896496,LevoParadoxIsomer,0.8,neutral,0.5
+498,2,98,863743810,HFLevo,0.0,neutral,nan
+498,2,98,863743810,LevoParadoxIsomer,0.0,neutral,0.5
+499,2,99,1908673249,HFLevo,2.0,neutral,nan
+499,2,99,1908673249,LevoParadoxIsomer,-0.8,overcautious,0.6000000238418579
+500,2,100,1039141574,HFLevo,0.0,neutral,nan
+500,2,100,1039141574,LevoParadoxIsomer,0.8,neutral,0.5
+501,2,101,1051567495,HFLevo,1.2,neutral,nan
+501,2,101,1051567495,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+502,2,102,837394817,HFLevo,0.8,neutral,nan
+502,2,102,837394817,LevoParadoxIsomer,0.8,neutral,0.5
+503,2,103,2013481887,HFLevo,0.0,neutral,nan
+503,2,103,2013481887,LevoParadoxIsomer,0.8,neutral,0.5
+504,2,104,438355621,HFLevo,0.8,neutral,nan
+504,2,104,438355621,LevoParadoxIsomer,0.0,neutral,0.5
+505,2,105,993080718,HFLevo,0.0,neutral,nan
+505,2,105,993080718,LevoParadoxIsomer,0.8,neutral,0.5
+506,2,106,1682379032,HFLevo,0.0,neutral,nan
+506,2,106,1682379032,LevoParadoxIsomer,0.8,neutral,0.5
+507,2,107,1013432362,HFLevo,0.8,neutral,nan
+507,2,107,1013432362,LevoParadoxIsomer,0.0,neutral,0.5
+508,2,108,1856562955,HFLevo,0.0,neutral,nan
+508,2,108,1856562955,LevoParadoxIsomer,0.0,neutral,0.5
+509,2,109,1667805176,HFLevo,1.2,neutral,nan
+509,2,109,1667805176,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+510,2,110,506300911,HFLevo,0.0,neutral,nan
+510,2,110,506300911,LevoParadoxIsomer,0.8,neutral,0.5
+511,2,111,222604131,HFLevo,0.8,neutral,nan
+511,2,111,222604131,LevoParadoxIsomer,0.0,neutral,0.5
+512,2,112,804599567,HFLevo,0.0,neutral,nan
+512,2,112,804599567,LevoParadoxIsomer,0.8,neutral,0.5
+513,2,113,1492613445,HFLevo,0.0,neutral,nan
+513,2,113,1492613445,LevoParadoxIsomer,0.8,neutral,0.5
+514,2,114,552650077,HFLevo,1.2,neutral,nan
+514,2,114,552650077,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+515,2,115,930025102,HFLevo,0.0,neutral,nan
+515,2,115,930025102,LevoParadoxIsomer,0.8,neutral,0.5
+516,2,116,873459449,HFLevo,-0.8,overcautious,nan
+516,2,116,873459449,LevoParadoxIsomer,0.0,neutral,0.6500000357627869
+517,2,117,467662889,HFLevo,0.0,neutral,nan
+517,2,117,467662889,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+518,2,118,445679122,HFLevo,0.0,neutral,nan
+518,2,118,445679122,LevoParadoxIsomer,0.0,neutral,0.5
+519,2,119,1328599419,HFLevo,2.0,neutral,nan
+519,2,119,1328599419,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+520,2,120,1323806708,HFLevo,0.0,neutral,nan
+520,2,120,1323806708,LevoParadoxIsomer,0.0,neutral,0.5
+521,2,121,1034589592,HFLevo,0.0,neutral,nan
+521,2,121,1034589592,LevoParadoxIsomer,0.8,neutral,0.5
+522,2,122,413811598,HFLevo,0.0,neutral,nan
+522,2,122,413811598,LevoParadoxIsomer,0.8,neutral,0.5
+523,2,123,753550936,HFLevo,0.0,neutral,nan
+523,2,123,753550936,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+524,2,124,1202521986,HFLevo,0.8,neutral,nan
+524,2,124,1202521986,LevoParadoxIsomer,0.8,neutral,0.5
+525,2,125,1933381577,HFLevo,-0.8,overcautious,nan
+525,2,125,1933381577,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+526,2,126,1963329040,HFLevo,0.0,neutral,nan
+526,2,126,1963329040,LevoParadoxIsomer,0.8,neutral,0.5
+527,2,127,1399821071,HFLevo,2.0,neutral,nan
+527,2,127,1399821071,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+528,2,128,1828929971,HFLevo,-3.0,overconfident,nan
+528,2,128,1828929971,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+529,2,129,1100686433,HFLevo,0.0,neutral,nan
+529,2,129,1100686433,LevoParadoxIsomer,0.8,neutral,0.5
+530,2,130,423774818,HFLevo,0.0,neutral,nan
+530,2,130,423774818,LevoParadoxIsomer,0.0,neutral,0.5
+531,2,131,2047001195,HFLevo,0.0,neutral,nan
+531,2,131,2047001195,LevoParadoxIsomer,0.8,neutral,0.5
+532,2,132,127060851,HFLevo,0.8,neutral,nan
+532,2,132,127060851,LevoParadoxIsomer,0.0,neutral,0.5
+533,2,133,1430942851,HFLevo,1.2,neutral,nan
+533,2,133,1430942851,LevoParadoxIsomer,-3.0,overconfident,0.2999999523162842
+534,2,134,1194133788,HFLevo,2.0,neutral,nan
+534,2,134,1194133788,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+535,2,135,30246456,HFLevo,0.0,neutral,nan
+535,2,135,30246456,LevoParadoxIsomer,0.0,neutral,0.5
+536,2,136,1871301169,HFLevo,0.8,neutral,nan
+536,2,136,1871301169,LevoParadoxIsomer,0.0,neutral,0.5
+537,2,137,1724977905,HFLevo,1.2,neutral,nan
+537,2,137,1724977905,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+538,2,138,1717796771,HFLevo,0.0,neutral,nan
+538,2,138,1717796771,LevoParadoxIsomer,0.8,neutral,0.5
+539,2,139,1593858573,HFLevo,0.8,neutral,nan
+539,2,139,1593858573,LevoParadoxIsomer,0.0,neutral,0.5
+540,2,140,200663940,HFLevo,1.2,neutral,nan
+540,2,140,200663940,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+541,2,141,1366649604,HFLevo,0.0,neutral,nan
+541,2,141,1366649604,LevoParadoxIsomer,0.8,neutral,0.5
+542,2,142,1673519505,HFLevo,0.8,neutral,nan
+542,2,142,1673519505,LevoParadoxIsomer,0.0,neutral,0.5
+543,2,143,1177626327,HFLevo,0.8,neutral,nan
+543,2,143,1177626327,LevoParadoxIsomer,0.0,neutral,0.5
+544,2,144,262855495,HFLevo,0.8,neutral,nan
+544,2,144,262855495,LevoParadoxIsomer,0.8,neutral,0.5
+545,2,145,1035690989,HFLevo,-3.0,overconfident,nan
+545,2,145,1035690989,LevoParadoxIsomer,0.0,neutral,0.3999999761581421
+546,2,146,192999926,HFLevo,0.8,neutral,nan
+546,2,146,192999926,LevoParadoxIsomer,0.8,neutral,0.5
+547,2,147,1097961901,HFLevo,-0.8,overcautious,nan
+547,2,147,1097961901,LevoParadoxIsomer,2.0,neutral,0.6500000357627869
+548,2,148,1040646467,HFLevo,2.0,neutral,nan
+548,2,148,1040646467,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+549,2,149,1995566658,HFLevo,0.8,neutral,nan
+549,2,149,1995566658,LevoParadoxIsomer,0.8,neutral,0.5
+550,2,150,390674833,HFLevo,0.0,neutral,nan
+550,2,150,390674833,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+551,2,151,662645968,HFLevo,0.0,neutral,nan
+551,2,151,662645968,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+552,2,152,111224037,HFLevo,2.0,neutral,nan
+552,2,152,111224037,LevoParadoxIsomer,0.0,neutral,0.7000000476837158
+553,2,153,1242319339,HFLevo,0.8,neutral,nan
+553,2,153,1242319339,LevoParadoxIsomer,0.8,neutral,0.5
+554,2,154,1295579201,HFLevo,0.0,neutral,nan
+554,2,154,1295579201,LevoParadoxIsomer,0.0,neutral,0.5
+555,2,155,88079789,HFLevo,0.8,neutral,nan
+555,2,155,88079789,LevoParadoxIsomer,0.8,neutral,0.5
+556,2,156,1218832160,HFLevo,0.8,neutral,nan
+556,2,156,1218832160,LevoParadoxIsomer,0.0,neutral,0.5
+557,2,157,1199450774,HFLevo,0.0,neutral,nan
+557,2,157,1199450774,LevoParadoxIsomer,0.8,neutral,0.5
+558,2,158,93920744,HFLevo,0.8,neutral,nan
+558,2,158,93920744,LevoParadoxIsomer,0.0,neutral,0.5
+559,2,159,301211568,HFLevo,0.0,neutral,nan
+559,2,159,301211568,LevoParadoxIsomer,0.0,neutral,0.5
+560,2,160,606831608,HFLevo,0.0,neutral,nan
+560,2,160,606831608,LevoParadoxIsomer,0.8,neutral,0.5
+561,2,161,248267762,HFLevo,2.0,neutral,nan
+561,2,161,248267762,LevoParadoxIsomer,0.0,neutral,0.7500000596046448
+562,2,162,479831997,HFLevo,0.0,neutral,nan
+562,2,162,479831997,LevoParadoxIsomer,0.8,neutral,0.5
+563,2,163,443794762,HFLevo,-3.0,overconfident,nan
+563,2,163,443794762,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+564,2,164,317845088,HFLevo,0.8,neutral,nan
+564,2,164,317845088,LevoParadoxIsomer,0.8,neutral,0.5
+565,2,165,976368810,HFLevo,-3.0,overconfident,nan
+565,2,165,976368810,LevoParadoxIsomer,0.0,neutral,0.24999995529651642
+566,2,166,1096060710,HFLevo,1.2,neutral,nan
+566,2,166,1096060710,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+567,2,167,1786432503,HFLevo,0.0,neutral,nan
+567,2,167,1786432503,LevoParadoxIsomer,0.0,neutral,0.5
+568,2,168,92915493,HFLevo,0.8,neutral,nan
+568,2,168,92915493,LevoParadoxIsomer,0.0,neutral,0.5
+569,2,169,169421372,HFLevo,0.8,neutral,nan
+569,2,169,169421372,LevoParadoxIsomer,0.8,neutral,0.5
+570,2,170,827738447,HFLevo,2.0,neutral,nan
+570,2,170,827738447,LevoParadoxIsomer,-0.8,overcautious,0.6500000357627869
+571,2,171,91871503,HFLevo,0.0,neutral,nan
+571,2,171,91871503,LevoParadoxIsomer,0.8,neutral,0.5
+572,2,172,1181786707,HFLevo,1.2,neutral,nan
+572,2,172,1181786707,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+573,2,173,14354338,HFLevo,0.0,neutral,nan
+573,2,173,14354338,LevoParadoxIsomer,0.0,neutral,0.5
+574,2,174,2140443672,HFLevo,-3.0,overconfident,nan
+574,2,174,2140443672,LevoParadoxIsomer,-3.0,overconfident,0.3999999761581421
+575,2,175,318790932,HFLevo,2.0,neutral,nan
+575,2,175,318790932,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+576,2,176,721850175,HFLevo,-0.8,overcautious,nan
+576,2,176,721850175,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+577,2,177,1030785601,HFLevo,0.0,neutral,nan
+577,2,177,1030785601,LevoParadoxIsomer,0.0,neutral,0.5
+578,2,178,1316251630,HFLevo,-0.8,overcautious,nan
+578,2,178,1316251630,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+579,2,179,1719447051,HFLevo,0.8,neutral,nan
+579,2,179,1719447051,LevoParadoxIsomer,0.0,neutral,0.5
+580,2,180,574594100,HFLevo,0.0,neutral,nan
+580,2,180,574594100,LevoParadoxIsomer,0.0,neutral,0.5
+581,2,181,2130613154,HFLevo,0.0,neutral,nan
+581,2,181,2130613154,LevoParadoxIsomer,0.0,neutral,0.5
+582,2,182,527701015,HFLevo,0.0,neutral,nan
+582,2,182,527701015,LevoParadoxIsomer,0.0,neutral,0.5
+583,2,183,1227208042,HFLevo,1.2,neutral,nan
+583,2,183,1227208042,LevoParadoxIsomer,0.0,neutral,0.2999999523162842
+584,2,184,633972706,HFLevo,0.0,neutral,nan
+584,2,184,633972706,LevoParadoxIsomer,0.8,neutral,0.5
+585,2,185,1481264686,HFLevo,0.0,neutral,nan
+585,2,185,1481264686,LevoParadoxIsomer,0.0,neutral,0.5
+586,2,186,1403332419,HFLevo,0.0,neutral,nan
+586,2,186,1403332419,LevoParadoxIsomer,0.0,neutral,0.5
+587,2,187,573328882,HFLevo,1.2,neutral,nan
+587,2,187,573328882,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+588,2,188,896430961,HFLevo,0.0,neutral,nan
+588,2,188,896430961,LevoParadoxIsomer,0.8,neutral,0.5
+589,2,189,861308429,HFLevo,0.0,neutral,nan
+589,2,189,861308429,LevoParadoxIsomer,0.0,neutral,0.34999996423721313
+590,2,190,857542805,HFLevo,1.2,neutral,nan
+590,2,190,857542805,LevoParadoxIsomer,-3.0,overconfident,0.2999999523162842
+591,2,191,555627012,HFLevo,0.8,neutral,nan
+591,2,191,555627012,LevoParadoxIsomer,0.0,neutral,0.5
+592,2,192,883473089,HFLevo,1.2,neutral,nan
+592,2,192,883473089,LevoParadoxIsomer,-3.0,overconfident,0.24999995529651642
+593,2,193,1183906577,HFLevo,2.0,neutral,nan
+593,2,193,1183906577,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+594,2,194,1295404722,HFLevo,2.0,neutral,nan
+594,2,194,1295404722,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+595,2,195,1602782174,HFLevo,0.0,neutral,nan
+595,2,195,1602782174,LevoParadoxIsomer,0.0,neutral,0.5
+596,2,196,1168170146,HFLevo,0.8,neutral,nan
+596,2,196,1168170146,LevoParadoxIsomer,0.8,neutral,0.5
+597,2,197,247049848,HFLevo,0.0,neutral,nan
+597,2,197,247049848,LevoParadoxIsomer,0.0,neutral,0.5
+598,2,198,1217521579,HFLevo,2.0,neutral,nan
+598,2,198,1217521579,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+599,2,199,1154221074,HFLevo,1.2,neutral,nan
+599,2,199,1154221074,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+600,2,200,1039822287,HFLevo,0.0,neutral,nan
+600,2,200,1039822287,LevoParadoxIsomer,0.0,neutral,0.5
+601,2,201,403774715,HFLevo,0.8,neutral,nan
+601,2,201,403774715,LevoParadoxIsomer,0.0,neutral,0.5
+602,2,202,1937799744,HFLevo,0.8,neutral,nan
+602,2,202,1937799744,LevoParadoxIsomer,0.0,neutral,0.5
+603,2,203,1530207800,HFLevo,0.8,neutral,nan
+603,2,203,1530207800,LevoParadoxIsomer,0.8,neutral,0.5
+604,2,204,899360822,HFLevo,0.8,neutral,nan
+604,2,204,899360822,LevoParadoxIsomer,0.8,neutral,0.5
+605,2,205,839140609,HFLevo,2.0,neutral,nan
+605,2,205,839140609,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+606,2,206,2141035731,HFLevo,2.0,neutral,nan
+606,2,206,2141035731,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+607,2,207,778084307,HFLevo,1.2,neutral,nan
+607,2,207,778084307,LevoParadoxIsomer,1.2,neutral,0.24999995529651642
+608,2,208,350945807,HFLevo,0.0,neutral,nan
+608,2,208,350945807,LevoParadoxIsomer,0.0,neutral,0.5
+609,2,209,970046052,HFLevo,2.0,neutral,nan
+609,2,209,970046052,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+610,2,210,828658713,HFLevo,1.2,neutral,nan
+610,2,210,828658713,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+611,2,211,899193899,HFLevo,0.8,neutral,nan
+611,2,211,899193899,LevoParadoxIsomer,0.0,neutral,0.5
+612,2,212,1918263815,HFLevo,1.2,neutral,nan
+612,2,212,1918263815,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+613,2,213,861402774,HFLevo,0.0,neutral,nan
+613,2,213,861402774,LevoParadoxIsomer,0.0,neutral,0.5
+614,2,214,545630730,HFLevo,0.0,neutral,nan
+614,2,214,545630730,LevoParadoxIsomer,0.8,neutral,0.5
+615,2,215,1771344806,HFLevo,0.0,neutral,nan
+615,2,215,1771344806,LevoParadoxIsomer,0.0,neutral,0.5
+616,2,216,835556689,HFLevo,2.0,neutral,nan
+616,2,216,835556689,LevoParadoxIsomer,-0.8,overcautious,0.7500000596046448
+617,2,217,920739262,HFLevo,0.8,neutral,nan
+617,2,217,920739262,LevoParadoxIsomer,0.8,neutral,0.5
+618,2,218,1075133249,HFLevo,0.8,neutral,nan
+618,2,218,1075133249,LevoParadoxIsomer,0.8,neutral,0.5
+619,2,219,1466130915,HFLevo,0.8,neutral,nan
+619,2,219,1466130915,LevoParadoxIsomer,0.0,neutral,0.5
+620,2,220,1545638251,HFLevo,0.0,neutral,nan
+620,2,220,1545638251,LevoParadoxIsomer,1.2,neutral,0.34999996423721313
+621,2,221,379504837,HFLevo,0.0,neutral,nan
+621,2,221,379504837,LevoParadoxIsomer,0.8,neutral,0.5
+622,2,222,833504022,HFLevo,0.8,neutral,nan
+622,2,222,833504022,LevoParadoxIsomer,0.8,neutral,0.5
+623,2,223,1846676001,HFLevo,1.2,neutral,nan
+623,2,223,1846676001,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+624,2,224,6416948,HFLevo,0.0,neutral,nan
+624,2,224,6416948,LevoParadoxIsomer,0.0,neutral,0.5
+625,2,225,1751117666,HFLevo,0.0,neutral,nan
+625,2,225,1751117666,LevoParadoxIsomer,0.8,neutral,0.5
+626,2,226,284570123,HFLevo,0.0,neutral,nan
+626,2,226,284570123,LevoParadoxIsomer,0.8,neutral,0.5
+627,2,227,189352376,HFLevo,0.0,neutral,nan
+627,2,227,189352376,LevoParadoxIsomer,0.8,neutral,0.5
+628,2,228,1370043294,HFLevo,0.0,neutral,nan
+628,2,228,1370043294,LevoParadoxIsomer,0.8,neutral,0.5
+629,2,229,1967776583,HFLevo,0.0,neutral,nan
+629,2,229,1967776583,LevoParadoxIsomer,0.8,neutral,0.5
+630,2,230,1013942334,HFLevo,0.8,neutral,nan
+630,2,230,1013942334,LevoParadoxIsomer,0.8,neutral,0.5
+631,2,231,600184202,HFLevo,0.0,neutral,nan
+631,2,231,600184202,LevoParadoxIsomer,0.0,neutral,0.5
+632,2,232,2085636407,HFLevo,0.8,neutral,nan
+632,2,232,2085636407,LevoParadoxIsomer,0.8,neutral,0.5
+633,2,233,328848547,HFLevo,0.8,neutral,nan
+633,2,233,328848547,LevoParadoxIsomer,0.0,neutral,0.5
+634,2,234,63425983,HFLevo,2.0,neutral,nan
+634,2,234,63425983,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+635,2,235,531264304,HFLevo,0.0,neutral,nan
+635,2,235,531264304,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+636,2,236,1531168957,HFLevo,0.8,neutral,nan
+636,2,236,1531168957,LevoParadoxIsomer,0.8,neutral,0.5
+637,2,237,1789312949,HFLevo,2.0,neutral,nan
+637,2,237,1789312949,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+638,2,238,1084402395,HFLevo,0.8,neutral,nan
+638,2,238,1084402395,LevoParadoxIsomer,0.0,neutral,0.5
+639,2,239,1545622175,HFLevo,0.8,neutral,nan
+639,2,239,1545622175,LevoParadoxIsomer,0.8,neutral,0.5
+640,2,240,999431888,HFLevo,0.0,neutral,nan
+640,2,240,999431888,LevoParadoxIsomer,0.8,neutral,0.5
+641,2,241,1725426490,HFLevo,0.8,neutral,nan
+641,2,241,1725426490,LevoParadoxIsomer,0.8,neutral,0.5
+642,2,242,669407095,HFLevo,0.8,neutral,nan
+642,2,242,669407095,LevoParadoxIsomer,0.8,neutral,0.5
+643,2,243,1417993163,HFLevo,0.0,neutral,nan
+643,2,243,1417993163,LevoParadoxIsomer,0.0,neutral,0.5
+644,2,244,1057045593,HFLevo,0.0,neutral,nan
+644,2,244,1057045593,LevoParadoxIsomer,0.8,neutral,0.5
+645,2,245,1380890934,HFLevo,0.8,neutral,nan
+645,2,245,1380890934,LevoParadoxIsomer,0.0,neutral,0.5
+646,2,246,238129855,HFLevo,0.0,neutral,nan
+646,2,246,238129855,LevoParadoxIsomer,0.8,neutral,0.5
+647,2,247,1486485713,HFLevo,0.8,neutral,nan
+647,2,247,1486485713,LevoParadoxIsomer,0.8,neutral,0.5
+648,2,248,30716145,HFLevo,0.0,neutral,nan
+648,2,248,30716145,LevoParadoxIsomer,0.8,neutral,0.5
+649,2,249,1615261593,HFLevo,0.0,neutral,nan
+649,2,249,1615261593,LevoParadoxIsomer,0.0,neutral,0.5
+650,2,250,2052819092,HFLevo,0.0,neutral,nan
+650,2,250,2052819092,LevoParadoxIsomer,0.8,neutral,0.5
+651,2,251,11185487,HFLevo,0.8,neutral,nan
+651,2,251,11185487,LevoParadoxIsomer,0.8,neutral,0.5
+652,2,252,928000143,HFLevo,-0.8,overcautious,nan
+652,2,252,928000143,LevoParadoxIsomer,-0.8,overcautious,0.8000000715255737
+653,2,253,2023963770,HFLevo,-3.0,overconfident,nan
+653,2,253,2023963770,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+654,2,254,1301736393,HFLevo,0.0,neutral,nan
+654,2,254,1301736393,LevoParadoxIsomer,0.8,neutral,0.5
+655,2,255,398352915,HFLevo,0.8,neutral,nan
+655,2,255,398352915,LevoParadoxIsomer,0.8,neutral,0.5
+656,2,256,299104752,HFLevo,0.8,neutral,nan
+656,2,256,299104752,LevoParadoxIsomer,0.8,neutral,0.5
+657,2,257,1900619662,HFLevo,2.0,neutral,nan
+657,2,257,1900619662,LevoParadoxIsomer,2.0,neutral,0.8500000834465027
+658,2,258,402834001,HFLevo,0.0,neutral,nan
+658,2,258,402834001,LevoParadoxIsomer,-3.0,overconfident,0.24999995529651642
+659,2,259,618191185,HFLevo,-3.0,overconfident,nan
+659,2,259,618191185,LevoParadoxIsomer,1.2,neutral,0.19999995827674866
+660,2,260,1107226955,HFLevo,2.0,neutral,nan
+660,2,260,1107226955,LevoParadoxIsomer,-0.8,overcautious,0.7000000476837158
+661,2,261,1079996997,HFLevo,0.8,neutral,nan
+661,2,261,1079996997,LevoParadoxIsomer,0.0,neutral,0.5
+662,2,262,1090799708,HFLevo,-0.8,overcautious,nan
+662,2,262,1090799708,LevoParadoxIsomer,2.0,neutral,0.8500000834465027
+663,2,263,614807561,HFLevo,1.2,neutral,nan
+663,2,263,614807561,LevoParadoxIsomer,0.0,neutral,0.19999995827674866
+664,2,264,1110540374,HFLevo,0.0,neutral,nan
+664,2,264,1110540374,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+665,2,265,230358128,HFLevo,0.0,neutral,nan
+665,2,265,230358128,LevoParadoxIsomer,0.0,neutral,0.5
+666,2,266,1307025172,HFLevo,0.8,neutral,nan
+666,2,266,1307025172,LevoParadoxIsomer,0.8,neutral,0.5
+667,2,267,2128513208,HFLevo,0.0,neutral,nan
+667,2,267,2128513208,LevoParadoxIsomer,-3.0,overconfident,0.19999995827674866
+668,2,268,1813190214,HFLevo,0.0,neutral,nan
+668,2,268,1813190214,LevoParadoxIsomer,-3.0,overconfident,0.19999995827674866
+669,2,269,427517589,HFLevo,2.0,neutral,nan
+669,2,269,427517589,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+670,2,270,971704768,HFLevo,0.0,neutral,nan
+670,2,270,971704768,LevoParadoxIsomer,0.8,neutral,0.5
+671,2,271,379120844,HFLevo,0.8,neutral,nan
+671,2,271,379120844,LevoParadoxIsomer,0.8,neutral,0.5
+672,2,272,366674027,HFLevo,2.0,neutral,nan
+672,2,272,366674027,LevoParadoxIsomer,2.0,neutral,0.7500000596046448
+673,2,273,1267589757,HFLevo,0.0,neutral,nan
+673,2,273,1267589757,LevoParadoxIsomer,0.0,neutral,0.5
+674,2,274,397609752,HFLevo,0.0,neutral,nan
+674,2,274,397609752,LevoParadoxIsomer,0.0,neutral,0.5
+675,2,275,544583423,HFLevo,0.8,neutral,nan
+675,2,275,544583423,LevoParadoxIsomer,0.0,neutral,0.5
+676,2,276,1765101426,HFLevo,0.0,neutral,nan
+676,2,276,1765101426,LevoParadoxIsomer,0.8,neutral,0.5
+677,2,277,569693597,HFLevo,2.0,neutral,nan
+677,2,277,569693597,LevoParadoxIsomer,-0.8,overcautious,0.8500000834465027
+678,2,278,189460983,HFLevo,0.0,neutral,nan
+678,2,278,189460983,LevoParadoxIsomer,0.0,neutral,0.5
+679,2,279,505940532,HFLevo,0.8,neutral,nan
+679,2,279,505940532,LevoParadoxIsomer,0.0,neutral,0.5
+680,2,280,961700536,HFLevo,-3.0,overconfident,nan
+680,2,280,961700536,LevoParadoxIsomer,-3.0,overconfident,0.34999996423721313
+681,2,281,932456279,HFLevo,2.0,neutral,nan
+681,2,281,932456279,LevoParadoxIsomer,-0.8,overcautious,0.7500000596046448
+682,2,282,390287427,HFLevo,2.0,neutral,nan
+682,2,282,390287427,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
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+684,2,284,436098708,HFLevo,0.8,neutral,nan
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+685,2,285,794629335,HFLevo,2.0,neutral,nan
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+686,2,286,1214082779,HFLevo,0.0,neutral,nan
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+687,2,287,1935214602,HFLevo,2.0,neutral,nan
+687,2,287,1935214602,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+688,2,288,284248471,HFLevo,0.8,neutral,nan
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+689,2,289,1230641234,HFLevo,0.0,neutral,nan
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+690,2,290,388858966,HFLevo,2.0,neutral,nan
+690,2,290,388858966,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+691,2,291,1021791854,HFLevo,2.0,neutral,nan
+691,2,291,1021791854,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+692,2,292,603303402,HFLevo,0.0,neutral,nan
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+693,2,293,862608619,HFLevo,0.0,neutral,nan
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+694,2,294,1651871421,HFLevo,0.8,neutral,nan
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+695,2,295,2051444313,HFLevo,0.0,neutral,nan
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+696,2,296,144628230,HFLevo,0.0,neutral,nan
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+697,2,297,1520429634,HFLevo,0.8,neutral,nan
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+699,2,299,1255956995,HFLevo,0.0,neutral,nan
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+700,2,300,208230768,HFLevo,0.8,neutral,nan
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+701,2,301,1460257335,HFLevo,0.0,neutral,nan
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+702,2,302,1011782590,HFLevo,0.8,neutral,nan
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+703,2,303,1789340557,HFLevo,2.0,neutral,nan
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+704,2,304,1869217819,HFLevo,0.8,neutral,nan
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+705,2,305,1673802110,HFLevo,0.8,neutral,nan
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+706,2,306,62463393,HFLevo,2.0,neutral,nan
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+707,2,307,2074841997,HFLevo,1.2,neutral,nan
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+708,2,308,1764116324,HFLevo,1.2,neutral,nan
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+709,2,309,1361763883,HFLevo,1.2,neutral,nan
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+710,2,310,2068149571,HFLevo,0.0,neutral,nan
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+711,2,311,1761270974,HFLevo,0.8,neutral,nan
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+713,2,313,527574022,HFLevo,0.0,neutral,nan
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+714,2,314,1948416385,HFLevo,0.0,neutral,nan
+714,2,314,1948416385,LevoParadoxIsomer,0.0,neutral,0.5
+715,2,315,842515162,HFLevo,1.2,neutral,nan
+715,2,315,842515162,LevoParadoxIsomer,-3.0,overconfident,0.1499999612569809
+716,2,316,728127582,HFLevo,0.8,neutral,nan
+716,2,316,728127582,LevoParadoxIsomer,0.8,neutral,0.5
+717,2,317,1807809684,HFLevo,2.0,neutral,nan
+717,2,317,1807809684,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+718,2,318,1911608906,HFLevo,0.8,neutral,nan
+718,2,318,1911608906,LevoParadoxIsomer,0.0,neutral,0.5
+719,2,319,507860031,HFLevo,0.0,neutral,nan
+719,2,319,507860031,LevoParadoxIsomer,0.0,neutral,0.5
+720,2,320,1559660249,HFLevo,0.8,neutral,nan
+720,2,320,1559660249,LevoParadoxIsomer,0.8,neutral,0.5
+721,2,321,817113483,HFLevo,0.0,neutral,nan
+721,2,321,817113483,LevoParadoxIsomer,0.0,neutral,0.5
+722,2,322,181612330,HFLevo,0.8,neutral,nan
+722,2,322,181612330,LevoParadoxIsomer,0.8,neutral,0.5
+723,2,323,330146939,HFLevo,0.0,neutral,nan
+723,2,323,330146939,LevoParadoxIsomer,0.8,neutral,0.5
+724,2,324,1451519916,HFLevo,0.0,neutral,nan
+724,2,324,1451519916,LevoParadoxIsomer,0.8,neutral,0.5
+725,2,325,429715005,HFLevo,0.0,neutral,nan
+725,2,325,429715005,LevoParadoxIsomer,0.8,neutral,0.5
+726,2,326,1836744305,HFLevo,2.0,neutral,nan
+726,2,326,1836744305,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+727,2,327,200999922,HFLevo,0.8,neutral,nan
+727,2,327,200999922,LevoParadoxIsomer,0.8,neutral,0.5
+728,2,328,686960989,HFLevo,1.2,neutral,nan
+728,2,328,686960989,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+729,2,329,2137759524,HFLevo,0.0,neutral,nan
+729,2,329,2137759524,LevoParadoxIsomer,0.0,neutral,0.5
+730,2,330,48470119,HFLevo,0.0,neutral,nan
+730,2,330,48470119,LevoParadoxIsomer,0.0,neutral,0.5
+731,2,331,1429722682,HFLevo,0.0,neutral,nan
+731,2,331,1429722682,LevoParadoxIsomer,0.0,neutral,0.5
+732,2,332,218074447,HFLevo,0.0,neutral,nan
+732,2,332,218074447,LevoParadoxIsomer,0.0,neutral,0.5
+733,2,333,2104545774,HFLevo,0.0,neutral,nan
+733,2,333,2104545774,LevoParadoxIsomer,0.0,neutral,0.5
+734,2,334,1816453390,HFLevo,0.0,neutral,nan
+734,2,334,1816453390,LevoParadoxIsomer,0.0,neutral,0.5
+735,2,335,639577193,HFLevo,0.8,neutral,nan
+735,2,335,639577193,LevoParadoxIsomer,0.0,neutral,0.5
+736,2,336,231860645,HFLevo,1.2,neutral,nan
+736,2,336,231860645,LevoParadoxIsomer,1.2,neutral,0.09999996423721313
+737,2,337,1142197128,HFLevo,0.0,neutral,nan
+737,2,337,1142197128,LevoParadoxIsomer,0.0,neutral,0.5
+738,2,338,1321680746,HFLevo,0.8,neutral,nan
+738,2,338,1321680746,LevoParadoxIsomer,0.8,neutral,0.5
+739,2,339,1746554905,HFLevo,0.0,neutral,nan
+739,2,339,1746554905,LevoParadoxIsomer,0.8,neutral,0.5
+740,2,340,396707811,HFLevo,0.0,neutral,nan
+740,2,340,396707811,LevoParadoxIsomer,0.8,neutral,0.5
+741,2,341,491514850,HFLevo,0.8,neutral,nan
+741,2,341,491514850,LevoParadoxIsomer,0.8,neutral,0.5
+742,2,342,461674718,HFLevo,2.0,neutral,nan
+742,2,342,461674718,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+743,2,343,1701500925,HFLevo,0.8,neutral,nan
+743,2,343,1701500925,LevoParadoxIsomer,0.0,neutral,0.5
+744,2,344,1344298913,HFLevo,0.8,neutral,nan
+744,2,344,1344298913,LevoParadoxIsomer,0.0,neutral,0.5
+745,2,345,1354823084,HFLevo,0.8,neutral,nan
+745,2,345,1354823084,LevoParadoxIsomer,0.0,neutral,0.5
+746,2,346,118160105,HFLevo,1.2,neutral,nan
+746,2,346,118160105,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+747,2,347,1044584312,HFLevo,0.8,neutral,nan
+747,2,347,1044584312,LevoParadoxIsomer,0.0,neutral,0.5
+748,2,348,1524392230,HFLevo,1.2,neutral,nan
+748,2,348,1524392230,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+749,2,349,428869864,HFLevo,0.8,neutral,nan
+749,2,349,428869864,LevoParadoxIsomer,0.8,neutral,0.5
+750,2,350,180994417,HFLevo,0.0,neutral,nan
+750,2,350,180994417,LevoParadoxIsomer,0.0,neutral,0.5
+751,2,351,885019589,HFLevo,0.8,neutral,nan
+751,2,351,885019589,LevoParadoxIsomer,0.8,neutral,0.5
+752,2,352,1231928222,HFLevo,0.8,neutral,nan
+752,2,352,1231928222,LevoParadoxIsomer,0.8,neutral,0.5
+753,2,353,1670237802,HFLevo,0.8,neutral,nan
+753,2,353,1670237802,LevoParadoxIsomer,0.8,neutral,0.5
+754,2,354,1414482049,HFLevo,0.0,neutral,nan
+754,2,354,1414482049,LevoParadoxIsomer,0.0,neutral,0.5
+755,2,355,1971477631,HFLevo,2.0,neutral,nan
+755,2,355,1971477631,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+756,2,356,883215094,HFLevo,-3.0,overconfident,nan
+756,2,356,883215094,LevoParadoxIsomer,1.2,neutral,0.09999996423721313
+757,2,357,343285816,HFLevo,1.2,neutral,nan
+757,2,357,343285816,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+758,2,358,1993664325,HFLevo,0.0,neutral,nan
+758,2,358,1993664325,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+759,2,359,538994041,HFLevo,0.0,neutral,nan
+759,2,359,538994041,LevoParadoxIsomer,0.8,neutral,0.5
+760,2,360,365720817,HFLevo,2.0,neutral,nan
+760,2,360,365720817,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+761,2,361,1931073180,HFLevo,0.0,neutral,nan
+761,2,361,1931073180,LevoParadoxIsomer,0.8,neutral,0.5
+762,2,362,330654991,HFLevo,0.0,neutral,nan
+762,2,362,330654991,LevoParadoxIsomer,0.0,neutral,0.5
+763,2,363,2117986561,HFLevo,-3.0,overconfident,nan
+763,2,363,2117986561,LevoParadoxIsomer,1.2,neutral,0.1499999612569809
+764,2,364,1745610389,HFLevo,2.0,neutral,nan
+764,2,364,1745610389,LevoParadoxIsomer,2.0,neutral,0.7000000476837158
+765,2,365,1935491097,HFLevo,0.8,neutral,nan
+765,2,365,1935491097,LevoParadoxIsomer,0.0,neutral,0.5
+766,2,366,816460352,HFLevo,0.8,neutral,nan
+766,2,366,816460352,LevoParadoxIsomer,0.8,neutral,0.5
+767,2,367,2034287239,HFLevo,0.0,neutral,nan
+767,2,367,2034287239,LevoParadoxIsomer,0.8,neutral,0.5
+768,2,368,1494357375,HFLevo,0.8,neutral,nan
+768,2,368,1494357375,LevoParadoxIsomer,0.0,neutral,0.5
+769,2,369,525099930,HFLevo,0.8,neutral,nan
+769,2,369,525099930,LevoParadoxIsomer,0.0,neutral,0.5
+770,2,370,461861420,HFLevo,0.8,neutral,nan
+770,2,370,461861420,LevoParadoxIsomer,0.8,neutral,0.5
+771,2,371,424434369,HFLevo,0.0,neutral,nan
+771,2,371,424434369,LevoParadoxIsomer,0.0,neutral,0.5
+772,2,372,1696040440,HFLevo,2.0,neutral,nan
+772,2,372,1696040440,LevoParadoxIsomer,2.0,neutral,0.8000000715255737
+773,2,373,1327732050,HFLevo,0.0,neutral,nan
+773,2,373,1327732050,LevoParadoxIsomer,0.0,neutral,0.5
+774,2,374,1275639253,HFLevo,0.8,neutral,nan
+774,2,374,1275639253,LevoParadoxIsomer,0.8,neutral,0.5
+775,2,375,180175624,HFLevo,0.8,neutral,nan
+775,2,375,180175624,LevoParadoxIsomer,0.0,neutral,0.5
+776,2,376,853325992,HFLevo,0.0,neutral,nan
+776,2,376,853325992,LevoParadoxIsomer,1.2,neutral,0.1499999612569809
+777,2,377,35314844,HFLevo,0.8,neutral,nan
+777,2,377,35314844,LevoParadoxIsomer,0.0,neutral,0.5
+778,2,378,512421119,HFLevo,2.0,neutral,nan
+778,2,378,512421119,LevoParadoxIsomer,2.0,neutral,0.9000000953674316
+779,2,379,1818877815,HFLevo,0.8,neutral,nan
+779,2,379,1818877815,LevoParadoxIsomer,0.8,neutral,0.5
+780,2,380,244612068,HFLevo,0.8,neutral,nan
+780,2,380,244612068,LevoParadoxIsomer,0.8,neutral,0.5
+781,2,381,438090890,HFLevo,0.0,neutral,nan
+781,2,381,438090890,LevoParadoxIsomer,0.8,neutral,0.5
+782,2,382,71908212,HFLevo,0.8,neutral,nan
+782,2,382,71908212,LevoParadoxIsomer,0.8,neutral,0.5
+783,2,383,763979621,HFLevo,0.8,neutral,nan
+783,2,383,763979621,LevoParadoxIsomer,0.0,neutral,0.5
+784,2,384,492409950,HFLevo,0.8,neutral,nan
+784,2,384,492409950,LevoParadoxIsomer,0.8,neutral,0.5
+785,2,385,1939330706,HFLevo,-3.0,overconfident,nan
+785,2,385,1939330706,LevoParadoxIsomer,1.2,neutral,0.2999999523162842
+786,2,386,304266562,HFLevo,0.8,neutral,nan
+786,2,386,304266562,LevoParadoxIsomer,0.8,neutral,0.5
+787,2,387,1839152413,HFLevo,0.0,neutral,nan
+787,2,387,1839152413,LevoParadoxIsomer,0.8,neutral,0.5
+788,2,388,1584964798,HFLevo,0.8,neutral,nan
+788,2,388,1584964798,LevoParadoxIsomer,0.0,neutral,0.5
+789,2,389,1705345529,HFLevo,0.0,neutral,nan
+789,2,389,1705345529,LevoParadoxIsomer,0.0,neutral,0.5
+790,2,390,530680927,HFLevo,0.0,neutral,nan
+790,2,390,530680927,LevoParadoxIsomer,-0.8,overcautious,0.8000000715255737
+791,2,391,601158382,HFLevo,1.2,neutral,nan
+791,2,391,601158382,LevoParadoxIsomer,-3.0,overconfident,0.09999996423721313
+792,2,392,661312537,HFLevo,0.8,neutral,nan
+792,2,392,661312537,LevoParadoxIsomer,0.0,neutral,0.5
+793,2,393,1099910893,HFLevo,0.0,neutral,nan
+793,2,393,1099910893,LevoParadoxIsomer,0.8,neutral,0.5
+794,2,394,1777836792,HFLevo,-3.0,overconfident,nan
+794,2,394,1777836792,LevoParadoxIsomer,-3.0,overconfident,0.049999963492155075
+795,2,395,320819501,HFLevo,0.0,neutral,nan
+795,2,395,320819501,LevoParadoxIsomer,0.8,neutral,0.5
+796,2,396,1262930064,HFLevo,0.8,neutral,nan
+796,2,396,1262930064,LevoParadoxIsomer,0.8,neutral,0.5
+797,2,397,1476382876,HFLevo,0.0,neutral,nan
+797,2,397,1476382876,LevoParadoxIsomer,0.0,neutral,0.5
+798,2,398,229480971,HFLevo,0.8,neutral,nan
+798,2,398,229480971,LevoParadoxIsomer,0.8,neutral,0.5
+799,2,399,1346765522,HFLevo,0.8,neutral,nan
+799,2,399,1346765522,LevoParadoxIsomer,0.8,neutral,0.5
+800,3,0,1015467875,HFLevo,1.2902930843845795,neutral,nan
+800,3,0,1015467875,LevoParadoxIsomer,0.49029308438457947,neutral,0.5
+801,3,1,871703027,HFLevo,0.2583186351063763,neutral,nan
+801,3,1,871703027,LevoParadoxIsomer,0.2583186351063763,neutral,0.5
+802,3,2,1121193219,HFLevo,-0.9285687801394484,overcautious,nan
+802,3,2,1121193219,LevoParadoxIsomer,-0.9285687801394484,overcautious,0.5
+803,3,3,1844331605,HFLevo,1.268712954493355,neutral,nan
+803,3,3,1844331605,LevoParadoxIsomer,2.068712954493355,neutral,0.5
+804,3,4,376821421,HFLevo,-2.1540989698282287,overconfident,nan
+804,3,4,376821421,LevoParadoxIsomer,1.0459010301717713,neutral,0.5
+805,3,5,1528947293,HFLevo,1.9236203197456223,neutral,nan
+805,3,5,1528947293,LevoParadoxIsomer,0.7236203197456224,neutral,0.5
+806,3,6,1457385050,HFLevo,-0.388878696540991,neutral,nan
+806,3,6,1457385050,LevoParadoxIsomer,-0.388878696540991,neutral,0.5
+807,3,7,1930833205,HFLevo,1.8947708652049988,neutral,nan
+807,3,7,1930833205,LevoParadoxIsomer,1.8947708652049988,neutral,0.5
+808,3,8,331658631,HFLevo,-0.34637226145235095,overcautious,nan
+808,3,8,331658631,LevoParadoxIsomer,2.4536277385476493,neutral,0.5
+809,3,9,1392760183,HFLevo,-1.7470900488231007,overconfident,nan
+809,3,9,1392760183,LevoParadoxIsomer,-1.7470900488231007,overconfident,0.5
+810,3,10,1410154195,HFLevo,-0.424961650515833,neutral,nan
+810,3,10,1410154195,LevoParadoxIsomer,0.37503834948416703,neutral,0.5
+811,3,11,1330897656,HFLevo,0.7046013636486836,neutral,nan
+811,3,11,1330897656,LevoParadoxIsomer,-1.2953986363513164,overconfident,0.5
+812,3,12,755426389,HFLevo,0.7155068417757497,neutral,nan
+812,3,12,755426389,LevoParadoxIsomer,0.7155068417757497,neutral,0.5
+813,3,13,478344337,HFLevo,0.5966519575139582,neutral,nan
+813,3,13,478344337,LevoParadoxIsomer,0.5966519575139582,neutral,0.5
+814,3,14,2135473267,HFLevo,-0.321538017298501,neutral,nan
+814,3,14,2135473267,LevoParadoxIsomer,-0.321538017298501,neutral,0.5
+815,3,15,1381920965,HFLevo,-0.8946149359899849,neutral,nan
+815,3,15,1381920965,LevoParadoxIsomer,-0.8946149359899849,neutral,0.5
+816,3,16,790353978,HFLevo,0.005089675891585976,neutral,nan
+816,3,16,790353978,LevoParadoxIsomer,0.005089675891585976,neutral,0.5
+817,3,17,886917887,HFLevo,1.1565439534575777,neutral,nan
+817,3,17,886917887,LevoParadoxIsomer,1.1565439534575777,neutral,0.44999998807907104
+818,3,18,1326287162,HFLevo,1.1505608969734533,neutral,nan
+818,3,18,1326287162,LevoParadoxIsomer,1.1505608969734533,neutral,0.5
+819,3,19,132467973,HFLevo,0.7640856467236494,neutral,nan
+819,3,19,132467973,LevoParadoxIsomer,-2.4359143532763508,overconfident,0.44999998807907104
+820,3,20,896694920,HFLevo,-0.16856310825832727,neutral,nan
+820,3,20,896694920,LevoParadoxIsomer,0.6314368917416728,neutral,0.5
+821,3,21,265295982,HFLevo,0.472286750236512,neutral,nan
+821,3,21,265295982,LevoParadoxIsomer,-0.32771324976348803,neutral,0.5
+822,3,22,188123431,HFLevo,0.7270211373649718,neutral,nan
+822,3,22,188123431,LevoParadoxIsomer,0.7270211373649718,neutral,0.5
+823,3,23,1385912200,HFLevo,-0.2257286159489377,neutral,nan
+823,3,23,1385912200,LevoParadoxIsomer,-0.2257286159489377,neutral,0.5
+824,3,24,449987729,HFLevo,-0.6492071611207221,overcautious,nan
+824,3,24,449987729,LevoParadoxIsomer,-0.6492071611207221,overcautious,0.5
+825,3,25,593302824,HFLevo,1.1549840686447408,neutral,nan
+825,3,25,593302824,LevoParadoxIsomer,-0.045015931355259255,neutral,0.44999998807907104
+826,3,26,194879931,HFLevo,0.17815411284413574,neutral,nan
+826,3,26,194879931,LevoParadoxIsomer,0.9781541128441358,neutral,0.5
+827,3,27,1758518115,HFLevo,0.24558567387383792,neutral,nan
+827,3,27,1758518115,LevoParadoxIsomer,0.24558567387383792,neutral,0.5
+828,3,28,1545311517,HFLevo,0.1627856125489353,neutral,nan
+828,3,28,1545311517,LevoParadoxIsomer,0.1627856125489353,neutral,0.5
+829,3,29,562603550,HFLevo,0.4183364147364109,neutral,nan
+829,3,29,562603550,LevoParadoxIsomer,1.2183364147364109,neutral,0.5
+830,3,30,1059808449,HFLevo,1.2917831804345576,neutral,nan
+830,3,30,1059808449,LevoParadoxIsomer,1.2917831804345576,neutral,0.5
+831,3,31,614383066,HFLevo,0.8163422218854106,neutral,nan
+831,3,31,614383066,LevoParadoxIsomer,0.016342221885410586,neutral,0.5
+832,3,32,1192512057,HFLevo,0.25576402304424295,neutral,nan
+832,3,32,1192512057,LevoParadoxIsomer,0.25576402304424295,neutral,0.5
+833,3,33,950581637,HFLevo,-0.8977958223217277,neutral,nan
+833,3,33,950581637,LevoParadoxIsomer,-0.09779582232172768,neutral,0.5
+834,3,34,1863772308,HFLevo,1.253089981409791,neutral,nan
+834,3,34,1863772308,LevoParadoxIsomer,0.45308998140979106,neutral,0.5
+835,3,35,1007304414,HFLevo,0.5254077508874286,neutral,nan
+835,3,35,1007304414,LevoParadoxIsomer,0.5254077508874286,neutral,0.5
+836,3,36,809066309,HFLevo,1.688901367855694,neutral,nan
+836,3,36,809066309,LevoParadoxIsomer,1.688901367855694,neutral,0.550000011920929
+837,3,37,1514006645,HFLevo,0.8581530848584542,neutral,nan
+837,3,37,1514006645,LevoParadoxIsomer,0.8581530848584542,neutral,0.44999998807907104
+838,3,38,1146495174,HFLevo,-1.0942163006184566,neutral,nan
+838,3,38,1146495174,LevoParadoxIsomer,-1.0942163006184566,neutral,0.5
+839,3,39,1859928275,HFLevo,2.5861061474664324,neutral,nan
+839,3,39,1859928275,LevoParadoxIsomer,-0.21389385253356752,overcautious,0.5
+840,3,40,303035184,HFLevo,-0.5964859730416497,overcautious,nan
+840,3,40,303035184,LevoParadoxIsomer,0.2035140269583503,neutral,0.550000011920929
+841,3,41,1724145893,HFLevo,0.19388739728817525,neutral,nan
+841,3,41,1724145893,LevoParadoxIsomer,0.19388739728817525,neutral,0.5
+842,3,42,1035035036,HFLevo,2.053749158155912,neutral,nan
+842,3,42,1035035036,LevoParadoxIsomer,-0.746250841844088,overcautious,0.550000011920929
+843,3,43,278635683,HFLevo,-0.4576999329201501,neutral,nan
+843,3,43,278635683,LevoParadoxIsomer,-1.2576999329201501,overcautious,0.550000011920929
+844,3,44,377801781,HFLevo,0.26696367777414026,neutral,nan
+844,3,44,377801781,LevoParadoxIsomer,-0.5330363222258598,overcautious,0.550000011920929
+845,3,45,1487506785,HFLevo,-0.22698718726653985,neutral,nan
+845,3,45,1487506785,LevoParadoxIsomer,0.5730128127334602,neutral,0.5
+846,3,46,766738768,HFLevo,1.7451958636224911,neutral,nan
+846,3,46,766738768,LevoParadoxIsomer,0.5451958636224912,neutral,0.44999998807907104
+847,3,47,1469590444,HFLevo,-0.11980525970062122,neutral,nan
+847,3,47,1469590444,LevoParadoxIsomer,0.6801947402993789,neutral,0.5
+848,3,48,1527511243,HFLevo,-1.5257325358366638,overconfident,nan
+848,3,48,1527511243,LevoParadoxIsomer,-1.5257325358366638,overconfident,0.5
+849,3,49,1889868992,HFLevo,0.18740335053313484,neutral,nan
+849,3,49,1889868992,LevoParadoxIsomer,-0.6125966494668652,neutral,0.5
+850,3,50,808836466,HFLevo,1.4424045317149554,neutral,nan
+850,3,50,808836466,LevoParadoxIsomer,0.6424045317149553,neutral,0.5
+851,3,51,880209119,HFLevo,-0.41698242562591825,neutral,nan
+851,3,51,880209119,LevoParadoxIsomer,0.3830175743740818,neutral,0.5
+852,3,52,2069774123,HFLevo,1.1358277140176187,neutral,nan
+852,3,52,2069774123,LevoParadoxIsomer,1.1358277140176187,neutral,0.5
+853,3,53,59978537,HFLevo,0.4273143926451667,neutral,nan
+853,3,53,59978537,LevoParadoxIsomer,1.2273143926451668,neutral,0.5
+854,3,54,268314216,HFLevo,-0.09615231078263872,neutral,nan
+854,3,54,268314216,LevoParadoxIsomer,-2.0961523107826388,overconfident,0.3999999761581421
+855,3,55,786773386,HFLevo,1.2293653945237177,neutral,nan
+855,3,55,786773386,LevoParadoxIsomer,0.42936539452371775,neutral,0.5
+856,3,56,1078804137,HFLevo,-0.0031176348126684796,neutral,nan
+856,3,56,1078804137,LevoParadoxIsomer,1.9968823651873315,neutral,0.6000000238418579
+857,3,57,88529032,HFLevo,0.9139455244229703,neutral,nan
+857,3,57,88529032,LevoParadoxIsomer,0.9139455244229703,neutral,0.5
+858,3,58,733437594,HFLevo,0.1998437802015791,neutral,nan
+858,3,58,733437594,LevoParadoxIsomer,0.9998437802015792,neutral,0.5
+859,3,59,1895500654,HFLevo,0.3147969815448564,neutral,nan
+859,3,59,1895500654,LevoParadoxIsomer,1.1147969815448564,neutral,0.5
+860,3,60,1156794677,HFLevo,0.2822839034147425,neutral,nan
+860,3,60,1156794677,LevoParadoxIsomer,-0.5177160965852575,neutral,0.5
+861,3,61,1733698348,HFLevo,0.30837871111489873,neutral,nan
+861,3,61,1733698348,LevoParadoxIsomer,0.30837871111489873,neutral,0.5
+862,3,62,1987643195,HFLevo,0.8494336690825907,neutral,nan
+862,3,62,1987643195,LevoParadoxIsomer,0.8494336690825907,neutral,0.5
+863,3,63,968829821,HFLevo,1.4361346524157803,neutral,nan
+863,3,63,968829821,LevoParadoxIsomer,-1.3638653475842197,overcautious,0.6000000238418579
+864,3,64,2125839782,HFLevo,0.5216533205919234,neutral,nan
+864,3,64,2125839782,LevoParadoxIsomer,-0.2783466794080766,neutral,0.5
+865,3,65,1588322590,HFLevo,1.4909286951175467,neutral,nan
+865,3,65,1588322590,LevoParadoxIsomer,1.4909286951175467,neutral,0.5
+866,3,66,956367808,HFLevo,-0.13769164780464407,neutral,nan
+866,3,66,956367808,LevoParadoxIsomer,1.0623083521953558,neutral,0.34999996423721313
+867,3,67,2135886715,HFLevo,0.10203387100943626,neutral,nan
+867,3,67,2135886715,LevoParadoxIsomer,0.10203387100943626,neutral,0.5
+868,3,68,705297723,HFLevo,2.267886356355249,neutral,nan
+868,3,68,705297723,LevoParadoxIsomer,-0.5321136436447511,overcautious,0.6000000238418579
+869,3,69,493712258,HFLevo,-0.7200477872906951,neutral,nan
+869,3,69,493712258,LevoParadoxIsomer,-0.7200477872906951,neutral,0.5
+870,3,70,75420035,HFLevo,0.4007933704297794,neutral,nan
+870,3,70,75420035,LevoParadoxIsomer,0.4007933704297794,neutral,0.5
+871,3,71,1867815399,HFLevo,-1.475944225695721,overconfident,nan
+871,3,71,1867815399,LevoParadoxIsomer,1.724055774304279,neutral,0.44999998807907104
+872,3,72,509860178,HFLevo,-1.0158303213136595,neutral,nan
+872,3,72,509860178,LevoParadoxIsomer,-1.0158303213136595,neutral,0.5
+873,3,73,687658415,HFLevo,0.7555449868898535,neutral,nan
+873,3,73,687658415,LevoParadoxIsomer,-0.044455013110146525,neutral,0.5
+874,3,74,319346835,HFLevo,1.1336872360109915,neutral,nan
+874,3,74,319346835,LevoParadoxIsomer,0.33368723601099154,neutral,0.5
+875,3,75,985914893,HFLevo,2.047902947899302,neutral,nan
+875,3,75,985914893,LevoParadoxIsomer,0.8479029478993018,neutral,0.34999996423721313
+876,3,76,456914363,HFLevo,-0.5563823345326576,neutral,nan
+876,3,76,456914363,LevoParadoxIsomer,-0.5563823345326576,neutral,0.5
+877,3,77,1903234235,HFLevo,-0.6168972120738625,neutral,nan
+877,3,77,1903234235,LevoParadoxIsomer,-0.6168972120738625,neutral,0.5
+878,3,78,1104964239,HFLevo,-0.5537494140426967,neutral,nan
+878,3,78,1104964239,LevoParadoxIsomer,-0.5537494140426967,neutral,0.5
+879,3,79,800272976,HFLevo,1.0014376600959138,neutral,nan
+879,3,79,800272976,LevoParadoxIsomer,-0.19856233990408617,neutral,0.44999998807907104
+880,3,80,514077870,HFLevo,0.548889466402683,neutral,nan
+880,3,80,514077870,LevoParadoxIsomer,0.548889466402683,neutral,0.5
+881,3,81,2084198909,HFLevo,0.37575413134291114,neutral,nan
+881,3,81,2084198909,LevoParadoxIsomer,0.37575413134291114,neutral,0.5
+882,3,82,909365514,HFLevo,0.8573741369785153,neutral,nan
+882,3,82,909365514,LevoParadoxIsomer,1.6573741369785153,neutral,0.5
+883,3,83,1338386269,HFLevo,-2.0258053677846037,overconfident,nan
+883,3,83,1338386269,LevoParadoxIsomer,1.174194632215396,neutral,0.44999998807907104
+884,3,84,947429886,HFLevo,0.6408681653569512,neutral,nan
+884,3,84,947429886,LevoParadoxIsomer,0.6408681653569512,neutral,0.5
+885,3,85,58590834,HFLevo,0.524978671615042,neutral,nan
+885,3,85,58590834,LevoParadoxIsomer,1.324978671615042,neutral,0.5
+886,3,86,684669113,HFLevo,-0.03636158399340117,neutral,nan
+886,3,86,684669113,LevoParadoxIsomer,0.7636384160065989,neutral,0.5
+887,3,87,898917724,HFLevo,-1.2982161592441008,overcautious,nan
+887,3,87,898917724,LevoParadoxIsomer,-0.49821615924410073,neutral,0.6500000357627869
+888,3,88,548630351,HFLevo,0.5729339230581685,neutral,nan
+888,3,88,548630351,LevoParadoxIsomer,-0.2270660769418316,neutral,0.5
+889,3,89,1616758964,HFLevo,1.9765640849812711,neutral,nan
+889,3,89,1616758964,LevoParadoxIsomer,0.776564084981271,neutral,0.34999996423721313
+890,3,90,2082505679,HFLevo,0.10646213282367678,neutral,nan
+890,3,90,2082505679,LevoParadoxIsomer,-0.6935378671763233,neutral,0.5
+891,3,91,741581883,HFLevo,-0.0517050103645994,neutral,nan
+891,3,91,741581883,LevoParadoxIsomer,-0.8517050103645994,neutral,0.5
+892,3,92,393553070,HFLevo,-1.66724885658832,overconfident,nan
+892,3,92,393553070,LevoParadoxIsomer,-1.66724885658832,overconfident,0.44999998807907104
+893,3,93,890404536,HFLevo,0.11880035600180827,neutral,nan
+893,3,93,890404536,LevoParadoxIsomer,0.11880035600180827,neutral,0.5
+894,3,94,179225205,HFLevo,1.1151171438000786,neutral,nan
+894,3,94,179225205,LevoParadoxIsomer,1.1151171438000786,neutral,0.6500000357627869
+895,3,95,852483643,HFLevo,0.2428840098478961,neutral,nan
+895,3,95,852483643,LevoParadoxIsomer,1.0428840098478962,neutral,0.5
+896,3,96,596549345,HFLevo,0.24399029764900915,neutral,nan
+896,3,96,596549345,LevoParadoxIsomer,1.0439902976490092,neutral,0.5
+897,3,97,1246421089,HFLevo,1.363801434612391,neutral,nan
+897,3,97,1246421089,LevoParadoxIsomer,1.363801434612391,neutral,0.3999999761581421
+898,3,98,505921964,HFLevo,1.341472357060377,neutral,nan
+898,3,98,505921964,LevoParadoxIsomer,1.341472357060377,neutral,0.34999996423721313
+899,3,99,1670483738,HFLevo,0.36815436045097727,neutral,nan
+899,3,99,1670483738,LevoParadoxIsomer,2.368154360450977,neutral,0.6500000357627869
+900,3,100,598294997,HFLevo,0.5186734793084872,neutral,nan
+900,3,100,598294997,LevoParadoxIsomer,0.5186734793084872,neutral,0.5
+901,3,101,1370406670,HFLevo,-0.6009999531995625,overcautious,nan
+901,3,101,1370406670,LevoParadoxIsomer,2.1990000468004376,neutral,0.6500000357627869
+902,3,102,1690182943,HFLevo,-0.7096418067537537,neutral,nan
+902,3,102,1690182943,LevoParadoxIsomer,-0.7096418067537537,neutral,0.44999998807907104
+903,3,103,1763960416,HFLevo,0.9006844579184998,neutral,nan
+903,3,103,1763960416,LevoParadoxIsomer,0.9006844579184998,neutral,0.5
+904,3,104,1645746969,HFLevo,-1.5486128539981796,overconfident,nan
+904,3,104,1645746969,LevoParadoxIsomer,1.6513871460018201,neutral,0.44999998807907104
+905,3,105,820931753,HFLevo,0.962884542235,neutral,nan
+905,3,105,820931753,LevoParadoxIsomer,0.962884542235,neutral,0.5
+906,3,106,1937660724,HFLevo,-1.7950840687430392,overconfident,nan
+906,3,106,1937660724,LevoParadoxIsomer,1.4049159312569608,neutral,0.3999999761581421
+907,3,107,536112434,HFLevo,-0.6515974640687232,neutral,nan
+907,3,107,536112434,LevoParadoxIsomer,0.14840253593127684,neutral,0.5
+908,3,108,852564596,HFLevo,-1.217424430883582,overconfident,nan
+908,3,108,852564596,LevoParadoxIsomer,-1.217424430883582,overconfident,0.34999996423721313
+909,3,109,2109579034,HFLevo,0.04948327477624494,neutral,nan
+909,3,109,2109579034,LevoParadoxIsomer,0.849483274776245,neutral,0.5
+910,3,110,1119218651,HFLevo,-0.43896650430096007,overcautious,nan
+910,3,110,1119218651,LevoParadoxIsomer,-0.43896650430096007,overcautious,0.6500000357627869
+911,3,111,1171844126,HFLevo,2.62092499583073,neutral,nan
+911,3,111,1171844126,LevoParadoxIsomer,2.62092499583073,neutral,0.6500000357627869
+912,3,112,1066832,HFLevo,0.40745352051750494,neutral,nan
+912,3,112,1066832,LevoParadoxIsomer,1.607453520517505,neutral,0.3999999761581421
+913,3,113,2039855300,HFLevo,1.695118384619502,neutral,nan
+913,3,113,2039855300,LevoParadoxIsomer,0.495118384619502,neutral,0.2999999523162842
+914,3,114,1830413706,HFLevo,0.6645794180980706,neutral,nan
+914,3,114,1830413706,LevoParadoxIsomer,0.6645794180980706,neutral,0.5
+915,3,115,1536434038,HFLevo,-2.4062797861421963,overconfident,nan
+915,3,115,1536434038,LevoParadoxIsomer,0.7937202138578034,neutral,0.3999999761581421
+916,3,116,204181968,HFLevo,0.6050677080581792,neutral,nan
+916,3,116,204181968,LevoParadoxIsomer,-0.19493229194182082,neutral,0.5
+917,3,117,782074719,HFLevo,0.5091854411977584,neutral,nan
+917,3,117,782074719,LevoParadoxIsomer,1.3091854411977586,neutral,0.5
+918,3,118,1683677695,HFLevo,-2.133018434655278,overconfident,nan
+918,3,118,1683677695,LevoParadoxIsomer,1.0669815653447223,neutral,0.44999998807907104
+919,3,119,811003462,HFLevo,1.0318839391215868,neutral,nan
+919,3,119,811003462,LevoParadoxIsomer,0.23188393912158678,neutral,0.5
+920,3,120,1843531798,HFLevo,1.0588472647494347,neutral,nan
+920,3,120,1843531798,LevoParadoxIsomer,-2.1411527352505653,overconfident,0.44999998807907104
+921,3,121,244956048,HFLevo,0.13156308418899432,neutral,nan
+921,3,121,244956048,LevoParadoxIsomer,-1.0684369158110056,neutral,0.3999999761581421
+922,3,122,521744775,HFLevo,0.47193103591872976,neutral,nan
+922,3,122,521744775,LevoParadoxIsomer,1.2719310359187297,neutral,0.5
+923,3,123,137476059,HFLevo,0.7847373937495904,neutral,nan
+923,3,123,137476059,LevoParadoxIsomer,2.7847373937495905,neutral,0.6500000357627869
+924,3,124,1493249395,HFLevo,0.7601033690157201,neutral,nan
+924,3,124,1493249395,LevoParadoxIsomer,0.7601033690157201,neutral,0.5
+925,3,125,1658377656,HFLevo,0.794286174152496,neutral,nan
+925,3,125,1658377656,LevoParadoxIsomer,-0.0057138258475040145,neutral,0.5
+926,3,126,1598149764,HFLevo,-0.7555320294233012,overcautious,nan
+926,3,126,1598149764,LevoParadoxIsomer,-0.7555320294233012,overcautious,0.6500000357627869
+927,3,127,1973871594,HFLevo,-0.9718334196844586,neutral,nan
+927,3,127,1973871594,LevoParadoxIsomer,-0.17183341968445853,neutral,0.5
+928,3,128,1575988774,HFLevo,1.1486204800254285,neutral,nan
+928,3,128,1575988774,LevoParadoxIsomer,0.34862048002542856,neutral,0.5
+929,3,129,352160276,HFLevo,2.704477659368699,neutral,nan
+929,3,129,352160276,LevoParadoxIsomer,0.7044776593686989,neutral,0.7000000476837158
+930,3,130,1951098323,HFLevo,2.4905644877611,neutral,nan
+930,3,130,1951098323,LevoParadoxIsomer,2.4905644877611,neutral,0.7000000476837158
+931,3,131,496971788,HFLevo,0.12521537161674298,neutral,nan
+931,3,131,496971788,LevoParadoxIsomer,0.12521537161674298,neutral,0.5
+932,3,132,1527624590,HFLevo,-0.5214567208411459,neutral,nan
+932,3,132,1527624590,LevoParadoxIsomer,0.27854327915885413,neutral,0.5
+933,3,133,400244675,HFLevo,0.4690883568339111,neutral,nan
+933,3,133,400244675,LevoParadoxIsomer,1.269088356833911,neutral,0.5
+934,3,134,386029926,HFLevo,1.6115419185654172,neutral,nan
+934,3,134,386029926,LevoParadoxIsomer,1.6115419185654172,neutral,0.5
+935,3,135,573115058,HFLevo,-0.46511297476372343,neutral,nan
+935,3,135,573115058,LevoParadoxIsomer,-0.46511297476372343,neutral,0.5
+936,3,136,575530726,HFLevo,-0.4869844334164761,neutral,nan
+936,3,136,575530726,LevoParadoxIsomer,-1.2869844334164762,neutral,0.5
+937,3,137,1964806113,HFLevo,0.8466030235631011,neutral,nan
+937,3,137,1964806113,LevoParadoxIsomer,0.8466030235631011,neutral,0.5
+938,3,138,1773149752,HFLevo,1.3243703157591027,neutral,nan
+938,3,138,1773149752,LevoParadoxIsomer,-0.6756296842408973,neutral,0.6000000238418579
+939,3,139,1241865463,HFLevo,-0.04740295264952519,neutral,nan
+939,3,139,1241865463,LevoParadoxIsomer,0.7525970473504748,neutral,0.5
+940,3,140,330338515,HFLevo,1.344359524627644,neutral,nan
+940,3,140,330338515,LevoParadoxIsomer,1.344359524627644,neutral,0.2999999523162842
+941,3,141,761423743,HFLevo,1.498481325424641,neutral,nan
+941,3,141,761423743,LevoParadoxIsomer,1.498481325424641,neutral,0.7000000476837158
+942,3,142,455706626,HFLevo,0.34741717796209975,neutral,nan
+942,3,142,455706626,LevoParadoxIsomer,-0.4525828220379003,neutral,0.5
+943,3,143,1509528211,HFLevo,0.26559561736173787,neutral,nan
+943,3,143,1509528211,LevoParadoxIsomer,0.26559561736173787,neutral,0.5
+944,3,144,1356675324,HFLevo,0.16250138667109204,neutral,nan
+944,3,144,1356675324,LevoParadoxIsomer,0.16250138667109204,neutral,0.3999999761581421
+945,3,145,567724229,HFLevo,0.029562369865580456,neutral,nan
+945,3,145,567724229,LevoParadoxIsomer,1.2295623698655804,neutral,0.3999999761581421
+946,3,146,490459625,HFLevo,-0.4252469944816251,neutral,nan
+946,3,146,490459625,LevoParadoxIsomer,0.3747530055183749,neutral,0.5
+947,3,147,1407038082,HFLevo,-0.4074473579795859,neutral,nan
+947,3,147,1407038082,LevoParadoxIsomer,-0.4074473579795859,neutral,0.5
+948,3,148,247701853,HFLevo,0.8320849787931718,neutral,nan
+948,3,148,247701853,LevoParadoxIsomer,1.6320849787931717,neutral,0.5
+949,3,149,1656318998,HFLevo,1.3550404894059949,neutral,nan
+949,3,149,1656318998,LevoParadoxIsomer,-1.844959510594005,overconfident,0.2999999523162842
+950,3,150,1050418889,HFLevo,1.487598882153529,neutral,nan
+950,3,150,1050418889,LevoParadoxIsomer,0.287598882153529,neutral,0.3999999761581421
+951,3,151,1647828121,HFLevo,-2.585520438420843,overconfident,nan
+951,3,151,1647828121,LevoParadoxIsomer,0.6144795615791568,neutral,0.24999995529651642
+952,3,152,1665251223,HFLevo,-0.34010762403862854,neutral,nan
+952,3,152,1665251223,LevoParadoxIsomer,0.4598923759613715,neutral,0.5
+953,3,153,1158119200,HFLevo,-2.301078011092654,overconfident,nan
+953,3,153,1158119200,LevoParadoxIsomer,0.8989219889073461,neutral,0.3999999761581421
+954,3,154,1745086003,HFLevo,0.1934767393490589,neutral,nan
+954,3,154,1745086003,LevoParadoxIsomer,0.1934767393490589,neutral,0.24999995529651642
+955,3,155,1329952935,HFLevo,0.4028712963253857,neutral,nan
+955,3,155,1329952935,LevoParadoxIsomer,0.4028712963253857,neutral,0.5
+956,3,156,129216577,HFLevo,0.8226525683891055,neutral,nan
+956,3,156,129216577,LevoParadoxIsomer,0.8226525683891055,neutral,0.5
+957,3,157,264447624,HFLevo,-0.2925163838635117,neutral,nan
+957,3,157,264447624,LevoParadoxIsomer,0.5074836161364884,neutral,0.5
+958,3,158,1239906804,HFLevo,-0.3826083405585227,neutral,nan
+958,3,158,1239906804,LevoParadoxIsomer,0.4173916594414773,neutral,0.5
+959,3,159,1968251934,HFLevo,0.13882640780924027,neutral,nan
+959,3,159,1968251934,LevoParadoxIsomer,0.13882640780924027,neutral,0.3999999761581421
+960,3,160,1710361518,HFLevo,0.24410679553415088,neutral,nan
+960,3,160,1710361518,LevoParadoxIsomer,0.24410679553415088,neutral,0.5
+961,3,161,1919087800,HFLevo,1.3080289495019592,neutral,nan
+961,3,161,1919087800,LevoParadoxIsomer,0.5080289495019592,neutral,0.5
+962,3,162,1038210496,HFLevo,-0.795060643378411,neutral,nan
+962,3,162,1038210496,LevoParadoxIsomer,-0.795060643378411,neutral,0.5
+963,3,163,1786593305,HFLevo,-0.4133444185482581,neutral,nan
+963,3,163,1786593305,LevoParadoxIsomer,-0.4133444185482581,neutral,0.5
+964,3,164,2098346951,HFLevo,0.37264748440059975,neutral,nan
+964,3,164,2098346951,LevoParadoxIsomer,1.1726474844005998,neutral,0.5
+965,3,165,1526904777,HFLevo,0.24330040162795474,neutral,nan
+965,3,165,1526904777,LevoParadoxIsomer,-0.5566995983720453,overcautious,0.6000000238418579
+966,3,166,1540522504,HFLevo,-0.48669767260032276,neutral,nan
+966,3,166,1540522504,LevoParadoxIsomer,-0.48669767260032276,neutral,0.5
+967,3,167,2109348201,HFLevo,0.6798652475070099,neutral,nan
+967,3,167,2109348201,LevoParadoxIsomer,0.6798652475070099,neutral,0.5
+968,3,168,1132622124,HFLevo,2.1098705387834524,neutral,nan
+968,3,168,1132622124,LevoParadoxIsomer,2.1098705387834524,neutral,0.7000000476837158
+969,3,169,1615465936,HFLevo,0.8935436568965417,neutral,nan
+969,3,169,1615465936,LevoParadoxIsomer,-0.30645634310345826,neutral,0.24999995529651642
+970,3,170,364326948,HFLevo,0.2815486952393436,neutral,nan
+970,3,170,364326948,LevoParadoxIsomer,1.0815486952393436,neutral,0.5
+971,3,171,1220814450,HFLevo,1.6723429375012457,neutral,nan
+971,3,171,1220814450,LevoParadoxIsomer,1.6723429375012457,neutral,0.5
+972,3,172,731873456,HFLevo,0.612363357663838,neutral,nan
+972,3,172,731873456,LevoParadoxIsomer,0.612363357663838,neutral,0.5
+973,3,173,1247536227,HFLevo,-0.007119122154074176,neutral,nan
+973,3,173,1247536227,LevoParadoxIsomer,-0.8071191221540742,neutral,0.5
+974,3,174,1337517349,HFLevo,0.7525185820761515,neutral,nan
+974,3,174,1337517349,LevoParadoxIsomer,-0.04748141792384848,neutral,0.5
+975,3,175,988735470,HFLevo,-1.0294181839139849,neutral,nan
+975,3,175,988735470,LevoParadoxIsomer,-0.22941818391398483,neutral,0.5
+976,3,176,624782456,HFLevo,1.008964131480814,neutral,nan
+976,3,176,624782456,LevoParadoxIsomer,1.008964131480814,neutral,0.5
+977,3,177,1614653143,HFLevo,-0.4293029498830948,neutral,nan
+977,3,177,1614653143,LevoParadoxIsomer,0.37069705011690524,neutral,0.5
+978,3,178,1500038813,HFLevo,1.1161432423615547,neutral,nan
+978,3,178,1500038813,LevoParadoxIsomer,1.9161432423615548,neutral,0.5
+979,3,179,2130149343,HFLevo,0.15174187528289917,neutral,nan
+979,3,179,2130149343,LevoParadoxIsomer,-0.6482581247171009,neutral,0.5
+980,3,180,132980447,HFLevo,0.33294699050354537,neutral,nan
+980,3,180,132980447,LevoParadoxIsomer,0.33294699050354537,neutral,0.7000000476837158
+981,3,181,1952726781,HFLevo,-0.43433634444053304,neutral,nan
+981,3,181,1952726781,LevoParadoxIsomer,0.7656636555594669,neutral,0.3999999761581421
+982,3,182,555625828,HFLevo,0.7003550926274642,neutral,nan
+982,3,182,555625828,LevoParadoxIsomer,-0.09964490737253578,neutral,0.5
+983,3,183,498814370,HFLevo,-0.2862536747479175,neutral,nan
+983,3,183,498814370,LevoParadoxIsomer,-0.2862536747479175,neutral,0.5
+984,3,184,206366738,HFLevo,0.8775826217617475,neutral,nan
+984,3,184,206366738,LevoParadoxIsomer,0.8775826217617475,neutral,0.5
+985,3,185,400273478,HFLevo,0.5164391358978044,neutral,nan
+985,3,185,400273478,LevoParadoxIsomer,0.5164391358978044,neutral,0.5
+986,3,186,670771760,HFLevo,0.14429033269177619,neutral,nan
+986,3,186,670771760,LevoParadoxIsomer,0.14429033269177619,neutral,0.5
+987,3,187,1820812129,HFLevo,0.3935862197370715,neutral,nan
+987,3,187,1820812129,LevoParadoxIsomer,1.1935862197370715,neutral,0.5
+988,3,188,1243239295,HFLevo,1.672818752501536,neutral,nan
+988,3,188,1243239295,LevoParadoxIsomer,1.672818752501536,neutral,0.6500000357627869
+989,3,189,1980464422,HFLevo,0.2808684424586057,neutral,nan
+989,3,189,1980464422,LevoParadoxIsomer,0.2808684424586057,neutral,0.5
+990,3,190,1156736002,HFLevo,-0.28270551923738596,neutral,nan
+990,3,190,1156736002,LevoParadoxIsomer,-0.28270551923738596,neutral,0.5
+991,3,191,1697044130,HFLevo,2.482642285510529,neutral,nan
+991,3,191,1697044130,LevoParadoxIsomer,2.482642285510529,neutral,0.7000000476837158
+992,3,192,562498219,HFLevo,0.07891803981678115,neutral,nan
+992,3,192,562498219,LevoParadoxIsomer,0.8789180398167812,neutral,0.5
+993,3,193,1878178784,HFLevo,0.20228392169117065,neutral,nan
+993,3,193,1878178784,LevoParadoxIsomer,0.20228392169117065,neutral,0.5
+994,3,194,2065152970,HFLevo,1.3065279618298171,neutral,nan
+994,3,194,2065152970,LevoParadoxIsomer,1.3065279618298171,neutral,0.5
+995,3,195,775550551,HFLevo,0.7688120776761941,neutral,nan
+995,3,195,775550551,LevoParadoxIsomer,-0.03118792232380599,neutral,0.5
+996,3,196,1056375082,HFLevo,0.6438854104142392,neutral,nan
+996,3,196,1056375082,LevoParadoxIsomer,2.643885410414239,neutral,0.7000000476837158
+997,3,197,1600998019,HFLevo,0.04270551606696389,neutral,nan
+997,3,197,1600998019,LevoParadoxIsomer,2.042705516066964,neutral,0.6500000357627869
+998,3,198,1027113059,HFLevo,0.6819952539956415,neutral,nan
+998,3,198,1027113059,LevoParadoxIsomer,0.6819952539956415,neutral,0.3999999761581421
+999,3,199,609138227,HFLevo,1.2826738383017549,neutral,nan
+999,3,199,609138227,LevoParadoxIsomer,1.2826738383017549,neutral,0.5
+1000,3,200,122699067,HFLevo,-1.7354079980637067,overconfident,nan
+1000,3,200,122699067,LevoParadoxIsomer,1.4645920019362932,neutral,0.3999999761581421
+1001,3,201,365381056,HFLevo,-1.8824601067255746,overconfident,nan
+1001,3,201,365381056,LevoParadoxIsomer,1.3175398932744253,neutral,0.24999995529651642
+1002,3,202,1238636582,HFLevo,1.5278968205399832,neutral,nan
+1002,3,202,1238636582,LevoParadoxIsomer,-1.2721031794600168,overcautious,0.6500000357627869
+1003,3,203,1658289347,HFLevo,-2.3608989109395417,overconfident,nan
+1003,3,203,1658289347,LevoParadoxIsomer,0.8391010890604582,neutral,0.3999999761581421
+1004,3,204,1322727040,HFLevo,-0.5812129563754665,neutral,nan
+1004,3,204,1322727040,LevoParadoxIsomer,-0.5812129563754665,neutral,0.5
+1005,3,205,2126230739,HFLevo,0.055286863165256224,neutral,nan
+1005,3,205,2126230739,LevoParadoxIsomer,0.8552868631652563,neutral,0.5
+1006,3,206,289701504,HFLevo,-0.6531967872653202,neutral,nan
+1006,3,206,289701504,LevoParadoxIsomer,0.1468032127346799,neutral,0.5
+1007,3,207,1261784629,HFLevo,0.9842492194028492,neutral,nan
+1007,3,207,1261784629,LevoParadoxIsomer,0.1842492194028491,neutral,0.5
+1008,3,208,1377412115,HFLevo,0.6245206594688067,neutral,nan
+1008,3,208,1377412115,LevoParadoxIsomer,0.6245206594688067,neutral,0.3999999761581421
+1009,3,209,1724786786,HFLevo,1.6985310433229601,neutral,nan
+1009,3,209,1724786786,LevoParadoxIsomer,0.8985310433229601,neutral,0.5
+1010,3,210,89300232,HFLevo,0.6123175354440541,neutral,nan
+1010,3,210,89300232,LevoParadoxIsomer,0.6123175354440541,neutral,0.5
+1011,3,211,1017426355,HFLevo,0.6029986467083133,neutral,nan
+1011,3,211,1017426355,LevoParadoxIsomer,1.4029986467083133,neutral,0.5
+1012,3,212,1395142654,HFLevo,-1.8787116845240532,overconfident,nan
+1012,3,212,1395142654,LevoParadoxIsomer,-1.8787116845240532,overconfident,0.3999999761581421
+1013,3,213,939770990,HFLevo,0.3690192392322482,neutral,nan
+1013,3,213,939770990,LevoParadoxIsomer,1.1690192392322483,neutral,0.5
+1014,3,214,1446797165,HFLevo,-0.757077484183034,overcautious,nan
+1014,3,214,1446797165,LevoParadoxIsomer,-0.757077484183034,overcautious,0.7000000476837158
+1015,3,215,464152649,HFLevo,1.46230012650082,neutral,nan
+1015,3,215,464152649,LevoParadoxIsomer,0.66230012650082,neutral,0.5
+1016,3,216,495168830,HFLevo,-0.5687465528107714,overcautious,nan
+1016,3,216,495168830,LevoParadoxIsomer,-0.5687465528107714,overcautious,0.7000000476837158
+1017,3,217,1239321323,HFLevo,0.7971781308027754,neutral,nan
+1017,3,217,1239321323,LevoParadoxIsomer,-0.0028218691972245677,neutral,0.5
+1018,3,218,1388207032,HFLevo,0.19859386994917153,neutral,nan
+1018,3,218,1388207032,LevoParadoxIsomer,0.19859386994917153,neutral,0.5
+1019,3,219,1731639298,HFLevo,0.7840663648071211,neutral,nan
+1019,3,219,1731639298,LevoParadoxIsomer,0.7840663648071211,neutral,0.5
+1020,3,220,1605648698,HFLevo,0.5776867006145332,neutral,nan
+1020,3,220,1605648698,LevoParadoxIsomer,-0.22231329938546687,neutral,0.5
+1021,3,221,281650086,HFLevo,0.1894277803351234,neutral,nan
+1021,3,221,281650086,LevoParadoxIsomer,0.9894277803351235,neutral,0.5
+1022,3,222,931350263,HFLevo,0.18787063742710997,neutral,nan
+1022,3,222,931350263,LevoParadoxIsomer,0.98787063742711,neutral,0.5
+1023,3,223,691328288,HFLevo,0.0743356805063284,neutral,nan
+1023,3,223,691328288,LevoParadoxIsomer,0.0743356805063284,neutral,0.5
+1024,3,224,633318446,HFLevo,1.8361888649880167,neutral,nan
+1024,3,224,633318446,LevoParadoxIsomer,1.8361888649880167,neutral,0.7500000596046448
+1025,3,225,1081427988,HFLevo,1.333833366616795,neutral,nan
+1025,3,225,1081427988,LevoParadoxIsomer,1.333833366616795,neutral,0.24999995529651642
+1026,3,226,115545717,HFLevo,0.5606678873993881,neutral,nan
+1026,3,226,115545717,LevoParadoxIsomer,0.5606678873993881,neutral,0.5
+1027,3,227,309528326,HFLevo,1.3826570077217273,neutral,nan
+1027,3,227,309528326,LevoParadoxIsomer,-1.4173429922782725,overcautious,0.7500000596046448
+1028,3,228,396994132,HFLevo,0.22439107870340078,neutral,nan
+1028,3,228,396994132,LevoParadoxIsomer,1.0243910787034007,neutral,0.5
+1029,3,229,1235689954,HFLevo,0.266905886575473,neutral,nan
+1029,3,229,1235689954,LevoParadoxIsomer,0.266905886575473,neutral,0.5
+1030,3,230,992703584,HFLevo,-0.4678753364966303,neutral,nan
+1030,3,230,992703584,LevoParadoxIsomer,0.3321246635033697,neutral,0.5
+1031,3,231,1080333835,HFLevo,1.6931666633730826,neutral,nan
+1031,3,231,1080333835,LevoParadoxIsomer,1.6931666633730826,neutral,0.34999996423721313
+1032,3,232,610075266,HFLevo,0.9761494721175346,neutral,nan
+1032,3,232,610075266,LevoParadoxIsomer,0.1761494721175345,neutral,0.5
+1033,3,233,131753921,HFLevo,0.543341071924465,neutral,nan
+1033,3,233,131753921,LevoParadoxIsomer,0.543341071924465,neutral,0.5
+1034,3,234,1547145187,HFLevo,0.7070829621692463,neutral,nan
+1034,3,234,1547145187,LevoParadoxIsomer,0.7070829621692463,neutral,0.5
+1035,3,235,1112526292,HFLevo,1.7173411436063477,neutral,nan
+1035,3,235,1112526292,LevoParadoxIsomer,1.7173411436063477,neutral,0.5
+1036,3,236,1114040609,HFLevo,0.33381345159447307,neutral,nan
+1036,3,236,1114040609,LevoParadoxIsomer,-0.466186548405527,neutral,0.5
+1037,3,237,9016300,HFLevo,0.5729399550735099,neutral,nan
+1037,3,237,9016300,LevoParadoxIsomer,-0.2270600449264901,neutral,0.5
+1038,3,238,2021796480,HFLevo,1.1871455134687214,neutral,nan
+1038,3,238,2021796480,LevoParadoxIsomer,0.38714551346872145,neutral,0.5
+1039,3,239,1032777022,HFLevo,0.36058618224120526,neutral,nan
+1039,3,239,1032777022,LevoParadoxIsomer,-1.6394138177587947,overconfident,0.3999999761581421
+1040,3,240,430409080,HFLevo,1.2729195184037565,neutral,nan
+1040,3,240,430409080,LevoParadoxIsomer,1.2729195184037565,neutral,0.5
+1041,3,241,1399360085,HFLevo,-2.6455916936013986,overconfident,nan
+1041,3,241,1399360085,LevoParadoxIsomer,0.5544083063986013,neutral,0.34999996423721313
+1042,3,242,436964639,HFLevo,-0.3121085626412692,overcautious,nan
+1042,3,242,436964639,LevoParadoxIsomer,2.487891437358731,neutral,0.8000000715255737
+1043,3,243,1154674875,HFLevo,1.0635383620472745,neutral,nan
+1043,3,243,1154674875,LevoParadoxIsomer,3.0,neutral,0.7500000596046448
+1044,3,244,1018060628,HFLevo,0.13100303028788035,neutral,nan
+1044,3,244,1018060628,LevoParadoxIsomer,0.9310030302878805,neutral,0.5
+1045,3,245,302524943,HFLevo,0.050867633534433285,neutral,nan
+1045,3,245,302524943,LevoParadoxIsomer,0.8508676335344333,neutral,0.5
+1046,3,246,993587315,HFLevo,-0.5280459100926949,neutral,nan
+1046,3,246,993587315,LevoParadoxIsomer,-0.5280459100926949,neutral,0.5
+1047,3,247,821789357,HFLevo,-1.1047095805351332,overcautious,nan
+1047,3,247,821789357,LevoParadoxIsomer,-1.1047095805351332,overcautious,0.7000000476837158
+1048,3,248,2140712286,HFLevo,0.9793875256939376,neutral,nan
+1048,3,248,2140712286,LevoParadoxIsomer,0.9793875256939376,neutral,0.5
+1049,3,249,791580549,HFLevo,1.7520582714410322,neutral,nan
+1049,3,249,791580549,LevoParadoxIsomer,-1.4479417285589675,overconfident,0.24999995529651642
+1050,3,250,1895531684,HFLevo,-0.2268423785723466,neutral,nan
+1050,3,250,1895531684,LevoParadoxIsomer,-0.2268423785723466,neutral,0.5
+1051,3,251,245446075,HFLevo,1.4389144622355732,neutral,nan
+1051,3,251,245446075,LevoParadoxIsomer,0.23891446223557314,neutral,0.34999996423721313
+1052,3,252,528694894,HFLevo,1.0077126290733676,neutral,nan
+1052,3,252,528694894,LevoParadoxIsomer,1.0077126290733676,neutral,0.5
+1053,3,253,383543867,HFLevo,0.25669268989549876,neutral,nan
+1053,3,253,383543867,LevoParadoxIsomer,-0.5433073101045013,neutral,0.5
+1054,3,254,507831984,HFLevo,-0.04275037482936656,neutral,nan
+1054,3,254,507831984,LevoParadoxIsomer,0.7572496251706334,neutral,0.5
+1055,3,255,1866575340,HFLevo,0.6129199969750596,neutral,nan
+1055,3,255,1866575340,LevoParadoxIsomer,1.4129199969750597,neutral,0.5
+1056,3,256,1604134655,HFLevo,0.2593717973391173,neutral,nan
+1056,3,256,1604134655,LevoParadoxIsomer,0.2593717973391173,neutral,0.5
+1057,3,257,2099757217,HFLevo,0.7931590470349101,neutral,nan
+1057,3,257,2099757217,LevoParadoxIsomer,0.7931590470349101,neutral,0.34999996423721313
+1058,3,258,113075702,HFLevo,-1.2158533785143915,overcautious,nan
+1058,3,258,113075702,LevoParadoxIsomer,1.5841466214856086,neutral,0.7500000596046448
+1059,3,259,1114107496,HFLevo,-2.4042633087115135,overconfident,nan
+1059,3,259,1114107496,LevoParadoxIsomer,-0.40426330871151356,neutral,0.34999996423721313
+1060,3,260,540143375,HFLevo,-0.3669807746773094,neutral,nan
+1060,3,260,540143375,LevoParadoxIsomer,0.43301922532269066,neutral,0.5
+1061,3,261,1516404,HFLevo,2.304766172762145,neutral,nan
+1061,3,261,1516404,LevoParadoxIsomer,2.304766172762145,neutral,0.8000000715255737
+1062,3,262,444535043,HFLevo,1.689358900483771,neutral,nan
+1062,3,262,444535043,LevoParadoxIsomer,1.689358900483771,neutral,0.34999996423721313
+1063,3,263,413354163,HFLevo,-0.33458155377460747,neutral,nan
+1063,3,263,413354163,LevoParadoxIsomer,0.4654184462253926,neutral,0.5
+1064,3,264,554663744,HFLevo,0.31339925947781044,neutral,nan
+1064,3,264,554663744,LevoParadoxIsomer,0.31339925947781044,neutral,0.5
+1065,3,265,903269569,HFLevo,0.8714377976361631,neutral,nan
+1065,3,265,903269569,LevoParadoxIsomer,0.8714377976361631,neutral,0.5
+1066,3,266,1932585699,HFLevo,1.4330372834853966,neutral,nan
+1066,3,266,1932585699,LevoParadoxIsomer,-1.7669627165146036,overconfident,0.19999995827674866
+1067,3,267,127998833,HFLevo,0.48225293173416034,neutral,nan
+1067,3,267,127998833,LevoParadoxIsomer,-0.3177470682658397,neutral,0.5
+1068,3,268,880556158,HFLevo,1.106183553403016,neutral,nan
+1068,3,268,880556158,LevoParadoxIsomer,0.30618355340301584,neutral,0.5
+1069,3,269,936001589,HFLevo,1.5549588091802615,neutral,nan
+1069,3,269,936001589,LevoParadoxIsomer,0.7549588091802616,neutral,0.5
+1070,3,270,1501301443,HFLevo,0.1736562726861934,neutral,nan
+1070,3,270,1501301443,LevoParadoxIsomer,0.1736562726861934,neutral,0.5
+1071,3,271,19383980,HFLevo,1.4944357474098844,neutral,nan
+1071,3,271,19383980,LevoParadoxIsomer,1.4944357474098844,neutral,0.5
+1072,3,272,1157426772,HFLevo,1.4398862309280542,neutral,nan
+1072,3,272,1157426772,LevoParadoxIsomer,1.4398862309280542,neutral,0.34999996423721313
+1073,3,273,1952228844,HFLevo,-0.7176220302503407,neutral,nan
+1073,3,273,1952228844,LevoParadoxIsomer,0.08237796974965939,neutral,0.5
+1074,3,274,1211030371,HFLevo,-1.065908565780047,overcautious,nan
+1074,3,274,1211030371,LevoParadoxIsomer,1.7340914342199532,neutral,0.7500000596046448
+1075,3,275,1321059392,HFLevo,-0.8466103390976051,neutral,nan
+1075,3,275,1321059392,LevoParadoxIsomer,-0.8466103390976051,neutral,0.5
+1076,3,276,1798791588,HFLevo,0.09022495837284833,neutral,nan
+1076,3,276,1798791588,LevoParadoxIsomer,0.09022495837284833,neutral,0.34999996423721313
+1077,3,277,1246246285,HFLevo,0.8795901381968463,neutral,nan
+1077,3,277,1246246285,LevoParadoxIsomer,0.07959013819684627,neutral,0.5
+1078,3,278,457172572,HFLevo,0.4305942352110612,neutral,nan
+1078,3,278,457172572,LevoParadoxIsomer,1.2305942352110613,neutral,0.5
+1079,3,279,448736337,HFLevo,0.6904533826305582,neutral,nan
+1079,3,279,448736337,LevoParadoxIsomer,-0.10954661736944181,neutral,0.5
+1080,3,280,1299406706,HFLevo,1.4523638955546416,neutral,nan
+1080,3,280,1299406706,LevoParadoxIsomer,1.4523638955546416,neutral,0.5
+1081,3,281,1264773565,HFLevo,0.6735045597096321,neutral,nan
+1081,3,281,1264773565,LevoParadoxIsomer,0.6735045597096321,neutral,0.5
+1082,3,282,1354099525,HFLevo,-0.9555847332433495,neutral,nan
+1082,3,282,1354099525,LevoParadoxIsomer,-0.9555847332433495,neutral,0.5
+1083,3,283,372058917,HFLevo,0.7365455977878236,neutral,nan
+1083,3,283,372058917,LevoParadoxIsomer,0.7365455977878236,neutral,0.5
+1084,3,284,1037785478,HFLevo,0.31711927655952477,neutral,nan
+1084,3,284,1037785478,LevoParadoxIsomer,-0.4828807234404753,neutral,0.5
+1085,3,285,1811624747,HFLevo,0.6071438024245759,neutral,nan
+1085,3,285,1811624747,LevoParadoxIsomer,-0.1928561975754241,neutral,0.5
+1086,3,286,1038158713,HFLevo,0.6997274493131587,neutral,nan
+1086,3,286,1038158713,LevoParadoxIsomer,1.4997274493131587,neutral,0.5
+1087,3,287,2093019788,HFLevo,-0.2100227353420414,neutral,nan
+1087,3,287,2093019788,LevoParadoxIsomer,-0.2100227353420414,neutral,0.5
+1088,3,288,212225853,HFLevo,-0.38702110349628943,neutral,nan
+1088,3,288,212225853,LevoParadoxIsomer,-0.38702110349628943,neutral,0.5
+1089,3,289,780127530,HFLevo,1.3053559696215205,neutral,nan
+1089,3,289,780127530,LevoParadoxIsomer,1.3053559696215205,neutral,0.5
+1090,3,290,708820677,HFLevo,-0.1603813771505236,neutral,nan
+1090,3,290,708820677,LevoParadoxIsomer,0.6396186228494765,neutral,0.5
+1091,3,291,1943826058,HFLevo,1.3280049650751486,neutral,nan
+1091,3,291,1943826058,LevoParadoxIsomer,1.3280049650751486,neutral,0.5
+1092,3,292,1228600845,HFLevo,2.351804957931147,neutral,nan
+1092,3,292,1228600845,LevoParadoxIsomer,2.351804957931147,neutral,0.7500000596046448
+1093,3,293,1907054980,HFLevo,1.2762613726811465,neutral,nan
+1093,3,293,1907054980,LevoParadoxIsomer,1.2762613726811465,neutral,0.5
+1094,3,294,684222005,HFLevo,2.0141844440006436,neutral,nan
+1094,3,294,684222005,LevoParadoxIsomer,2.0141844440006436,neutral,0.34999996423721313
+1095,3,295,1264548661,HFLevo,0.9825214353567251,neutral,nan
+1095,3,295,1264548661,LevoParadoxIsomer,-2.217478564643275,overconfident,0.34999996423721313
+1096,3,296,1936568871,HFLevo,1.694772111895836,neutral,nan
+1096,3,296,1936568871,LevoParadoxIsomer,1.694772111895836,neutral,0.7500000596046448
+1097,3,297,1497319181,HFLevo,0.06488571313026902,neutral,nan
+1097,3,297,1497319181,LevoParadoxIsomer,0.06488571313026902,neutral,0.5
+1098,3,298,97345709,HFLevo,0.530084295164116,neutral,nan
+1098,3,298,97345709,LevoParadoxIsomer,0.530084295164116,neutral,0.5
+1099,3,299,652059175,HFLevo,0.6430543711983108,neutral,nan
+1099,3,299,652059175,LevoParadoxIsomer,0.6430543711983108,neutral,0.5
+1100,3,300,130313659,HFLevo,1.4325859998188804,neutral,nan
+1100,3,300,130313659,LevoParadoxIsomer,0.6325859998188803,neutral,0.5
+1101,3,301,1373428940,HFLevo,0.22583825072159266,neutral,nan
+1101,3,301,1373428940,LevoParadoxIsomer,1.0258382507215926,neutral,0.5
+1102,3,302,1763220031,HFLevo,0.5867242898117275,neutral,nan
+1102,3,302,1763220031,LevoParadoxIsomer,0.5867242898117275,neutral,0.5
+1103,3,303,766053327,HFLevo,2.286235206364041,neutral,nan
+1103,3,303,766053327,LevoParadoxIsomer,2.286235206364041,neutral,0.7500000596046448
+1104,3,304,186108448,HFLevo,1.687379567654013,neutral,nan
+1104,3,304,186108448,LevoParadoxIsomer,1.687379567654013,neutral,0.7500000596046448
+1105,3,305,1525727770,HFLevo,1.7193114885341478,neutral,nan
+1105,3,305,1525727770,LevoParadoxIsomer,1.7193114885341478,neutral,0.7500000596046448
+1106,3,306,1723298298,HFLevo,1.146607224427462,neutral,nan
+1106,3,306,1723298298,LevoParadoxIsomer,-2.053392775572538,overconfident,0.2999999523162842
+1107,3,307,1809177412,HFLevo,0.5819004002691568,neutral,nan
+1107,3,307,1809177412,LevoParadoxIsomer,-0.21809959973084328,neutral,0.5
+1108,3,308,808471678,HFLevo,0.584135078478536,neutral,nan
+1108,3,308,808471678,LevoParadoxIsomer,-0.21586492152146408,neutral,0.5
+1109,3,309,1012658889,HFLevo,-0.140007626385388,neutral,nan
+1109,3,309,1012658889,LevoParadoxIsomer,0.659992373614612,neutral,0.5
+1110,3,310,123240018,HFLevo,0.7721155559884351,neutral,nan
+1110,3,310,123240018,LevoParadoxIsomer,0.7721155559884351,neutral,0.5
+1111,3,311,595923089,HFLevo,2.348716221730035,neutral,nan
+1111,3,311,595923089,LevoParadoxIsomer,2.348716221730035,neutral,0.7500000596046448
+1112,3,312,64946568,HFLevo,0.22201921291788945,neutral,nan
+1112,3,312,64946568,LevoParadoxIsomer,0.22201921291788945,neutral,0.5
+1113,3,313,1694054676,HFLevo,3.0,neutral,nan
+1113,3,313,1694054676,LevoParadoxIsomer,3.0,neutral,0.7500000596046448
+1114,3,314,1378532479,HFLevo,1.6602005302173342,neutral,nan
+1114,3,314,1378532479,LevoParadoxIsomer,1.6602005302173342,neutral,0.7500000596046448
+1115,3,315,294177744,HFLevo,0.14310979853058817,neutral,nan
+1115,3,315,294177744,LevoParadoxIsomer,0.9431097985305882,neutral,0.5
+1116,3,316,382616457,HFLevo,-0.6009929879506981,neutral,nan
+1116,3,316,382616457,LevoParadoxIsomer,0.19900701204930193,neutral,0.5
+1117,3,317,654022692,HFLevo,-0.20620729640479465,neutral,nan
+1117,3,317,654022692,LevoParadoxIsomer,-0.20620729640479465,neutral,0.5
+1118,3,318,1938298404,HFLevo,1.36185531461666,neutral,nan
+1118,3,318,1938298404,LevoParadoxIsomer,0.16185531461666003,neutral,0.24999995529651642
+1119,3,319,1251753220,HFLevo,1.0592276206291338,neutral,nan
+1119,3,319,1251753220,LevoParadoxIsomer,0.25922762062913374,neutral,0.5
+1120,3,320,164235737,HFLevo,1.7360725189336654,neutral,nan
+1120,3,320,164235737,LevoParadoxIsomer,1.7360725189336654,neutral,0.5
+1121,3,321,1176577515,HFLevo,-0.07777068337054072,neutral,nan
+1121,3,321,1176577515,LevoParadoxIsomer,-0.07777068337054072,neutral,0.5
+1122,3,322,66131236,HFLevo,0.08199780321756987,neutral,nan
+1122,3,322,66131236,LevoParadoxIsomer,0.8819978032175699,neutral,0.5
+1123,3,323,816614407,HFLevo,1.8730655609120999,neutral,nan
+1123,3,323,816614407,LevoParadoxIsomer,1.8730655609120999,neutral,0.7500000596046448
+1124,3,324,1678449735,HFLevo,1.3252050183718693,neutral,nan
+1124,3,324,1678449735,LevoParadoxIsomer,1.3252050183718693,neutral,0.5
+1125,3,325,155847418,HFLevo,0.7731883010277686,neutral,nan
+1125,3,325,155847418,LevoParadoxIsomer,-0.02681169897223142,neutral,0.5
+1126,3,326,385469728,HFLevo,2.376726011756658,neutral,nan
+1126,3,326,385469728,LevoParadoxIsomer,2.376726011756658,neutral,0.7500000596046448
+1127,3,327,2122723976,HFLevo,1.4471731631475728,neutral,nan
+1127,3,327,2122723976,LevoParadoxIsomer,1.4471731631475728,neutral,0.5
+1128,3,328,1933100203,HFLevo,1.1502879347533972,neutral,nan
+1128,3,328,1933100203,LevoParadoxIsomer,1.1502879347533972,neutral,0.24999995529651642
+1129,3,329,650925416,HFLevo,-0.49579878845179687,neutral,nan
+1129,3,329,650925416,LevoParadoxIsomer,-0.49579878845179687,neutral,0.5
+1130,3,330,1094127659,HFLevo,0.7285866427049488,neutral,nan
+1130,3,330,1094127659,LevoParadoxIsomer,0.7285866427049488,neutral,0.5
+1131,3,331,876865270,HFLevo,-2.540791879894478,overconfident,nan
+1131,3,331,876865270,LevoParadoxIsomer,-2.540791879894478,overconfident,0.1499999612569809
+1132,3,332,1319422772,HFLevo,1.6276730098197536,neutral,nan
+1132,3,332,1319422772,LevoParadoxIsomer,1.6276730098197536,neutral,0.8000000715255737
+1133,3,333,174465858,HFLevo,-0.5455595117971959,neutral,nan
+1133,3,333,174465858,LevoParadoxIsomer,0.2544404882028042,neutral,0.5
+1134,3,334,2060489424,HFLevo,0.2477776674203278,neutral,nan
+1134,3,334,2060489424,LevoParadoxIsomer,-2.952222332579672,overconfident,0.24999995529651642
+1135,3,335,1621988536,HFLevo,-0.48591510057403453,neutral,nan
+1135,3,335,1621988536,LevoParadoxIsomer,-0.48591510057403453,neutral,0.5
+1136,3,336,965810292,HFLevo,0.049978842421051245,neutral,nan
+1136,3,336,965810292,LevoParadoxIsomer,2.0499788424210514,neutral,0.8000000715255737
+1137,3,337,2093565938,HFLevo,2.4772099381580697,neutral,nan
+1137,3,337,2093565938,LevoParadoxIsomer,2.4772099381580697,neutral,0.8000000715255737
+1138,3,338,1650235702,HFLevo,0.1418919856891526,neutral,nan
+1138,3,338,1650235702,LevoParadoxIsomer,0.1418919856891526,neutral,0.5
+1139,3,339,511412748,HFLevo,-0.1653548431412035,neutral,nan
+1139,3,339,511412748,LevoParadoxIsomer,0.6346451568587965,neutral,0.5
+1140,3,340,922611389,HFLevo,1.938354801916106,neutral,nan
+1140,3,340,922611389,LevoParadoxIsomer,1.938354801916106,neutral,0.7500000596046448
+1141,3,341,652729866,HFLevo,1.148976720949957,neutral,nan
+1141,3,341,652729866,LevoParadoxIsomer,1.948976720949957,neutral,0.5
+1142,3,342,221600474,HFLevo,0.9973383239464882,neutral,nan
+1142,3,342,221600474,LevoParadoxIsomer,0.9973383239464882,neutral,0.5
+1143,3,343,31421970,HFLevo,0.02929702365773283,neutral,nan
+1143,3,343,31421970,LevoParadoxIsomer,0.02929702365773283,neutral,0.5
+1144,3,344,1192414163,HFLevo,0.6691241621227473,neutral,nan
+1144,3,344,1192414163,LevoParadoxIsomer,0.6691241621227473,neutral,0.5
+1145,3,345,649875877,HFLevo,-0.09296404124861374,neutral,nan
+1145,3,345,649875877,LevoParadoxIsomer,0.7070359587513864,neutral,0.5
+1146,3,346,105131269,HFLevo,-0.1475388756482452,overcautious,nan
+1146,3,346,105131269,LevoParadoxIsomer,0.6524611243517549,neutral,0.7500000596046448
+1147,3,347,156751511,HFLevo,0.6272225859885717,neutral,nan
+1147,3,347,156751511,LevoParadoxIsomer,0.6272225859885717,neutral,0.5
+1148,3,348,761801730,HFLevo,-0.5934301134706246,neutral,nan
+1148,3,348,761801730,LevoParadoxIsomer,0.20656988652937547,neutral,0.5
+1149,3,349,2081080430,HFLevo,0.5738879582462928,neutral,nan
+1149,3,349,2081080430,LevoParadoxIsomer,1.3738879582462928,neutral,0.5
+1150,3,350,2015553861,HFLevo,-1.4177993539631866,neutral,nan
+1150,3,350,2015553861,LevoParadoxIsomer,-1.4177993539631866,neutral,0.5
+1151,3,351,1908286303,HFLevo,1.506092772192412,neutral,nan
+1151,3,351,1908286303,LevoParadoxIsomer,2.306092772192412,neutral,0.5
+1152,3,352,911061556,HFLevo,1.3687399763929848,neutral,nan
+1152,3,352,911061556,LevoParadoxIsomer,0.5687399763929849,neutral,0.5
+1153,3,353,34499089,HFLevo,1.871734875882194,neutral,nan
+1153,3,353,34499089,LevoParadoxIsomer,1.871734875882194,neutral,0.8000000715255737
+1154,3,354,2098425720,HFLevo,-0.1085834687581021,neutral,nan
+1154,3,354,2098425720,LevoParadoxIsomer,-0.9085834687581021,neutral,0.5
+1155,3,355,1106840722,HFLevo,-0.24296696671554552,overcautious,nan
+1155,3,355,1106840722,LevoParadoxIsomer,2.5570330332844544,neutral,0.8000000715255737
+1156,3,356,317986024,HFLevo,-0.2724812485808378,neutral,nan
+1156,3,356,317986024,LevoParadoxIsomer,0.5275187514191623,neutral,0.5
+1157,3,357,129256349,HFLevo,-0.01562539491936473,neutral,nan
+1157,3,357,129256349,LevoParadoxIsomer,-0.01562539491936473,neutral,0.09999996423721313
+1158,3,358,431303789,HFLevo,0.071342761929298,overcautious,nan
+1158,3,358,431303789,LevoParadoxIsomer,0.071342761929298,overcautious,0.8000000715255737
+1159,3,359,523632446,HFLevo,0.08805085928707428,neutral,nan
+1159,3,359,523632446,LevoParadoxIsomer,0.08805085928707428,neutral,0.5
+1160,3,360,1950687447,HFLevo,0.26743054682592193,neutral,nan
+1160,3,360,1950687447,LevoParadoxIsomer,0.26743054682592193,neutral,0.5
+1161,3,361,1411833232,HFLevo,0.006337207980466301,neutral,nan
+1161,3,361,1411833232,LevoParadoxIsomer,0.8063372079804664,neutral,0.5
+1162,3,362,690319881,HFLevo,1.7649829989766284,neutral,nan
+1162,3,362,690319881,LevoParadoxIsomer,1.7649829989766284,neutral,0.09999996423721313
+1163,3,363,175246150,HFLevo,0.8602184244464084,neutral,nan
+1163,3,363,175246150,LevoParadoxIsomer,0.8602184244464084,neutral,0.34999996423721313
+1164,3,364,1182674932,HFLevo,1.7870325658814412,neutral,nan
+1164,3,364,1182674932,LevoParadoxIsomer,1.7870325658814412,neutral,0.19999995827674866
+1165,3,365,393595655,HFLevo,0.8496991941854053,neutral,nan
+1165,3,365,393595655,LevoParadoxIsomer,0.8496991941854053,neutral,0.5
+1166,3,366,1029071917,HFLevo,0.2292916064121996,neutral,nan
+1166,3,366,1029071917,LevoParadoxIsomer,0.2292916064121996,neutral,0.5
+1167,3,367,440607926,HFLevo,0.3759669685649799,neutral,nan
+1167,3,367,440607926,LevoParadoxIsomer,1.17596696856498,neutral,0.5
+1168,3,368,990295427,HFLevo,-0.0892679757615117,neutral,nan
+1168,3,368,990295427,LevoParadoxIsomer,-0.0892679757615117,neutral,0.5
+1169,3,369,1533915152,HFLevo,0.1294248189887542,neutral,nan
+1169,3,369,1533915152,LevoParadoxIsomer,0.1294248189887542,neutral,0.5
+1170,3,370,2028880655,HFLevo,0.2673159176286863,neutral,nan
+1170,3,370,2028880655,LevoParadoxIsomer,0.2673159176286863,neutral,0.5
+1171,3,371,1110082952,HFLevo,-0.14934540849956535,neutral,nan
+1171,3,371,1110082952,LevoParadoxIsomer,-0.14934540849956535,neutral,0.5
+1172,3,372,1351612215,HFLevo,-0.34334201334412834,neutral,nan
+1172,3,372,1351612215,LevoParadoxIsomer,0.4566579866558717,neutral,0.5
+1173,3,373,161873384,HFLevo,0.4425368930740302,neutral,nan
+1173,3,373,161873384,LevoParadoxIsomer,0.4425368930740302,neutral,0.5
+1174,3,374,1943568012,HFLevo,-1.0470027903099404,overcautious,nan
+1174,3,374,1943568012,LevoParadoxIsomer,1.7529972096900597,neutral,0.7500000596046448
+1175,3,375,27538724,HFLevo,-2.030138174818131,overconfident,nan
+1175,3,375,27538724,LevoParadoxIsomer,1.1698618251818689,neutral,0.09999996423721313
+1176,3,376,534070892,HFLevo,0.204998638571319,neutral,nan
+1176,3,376,534070892,LevoParadoxIsomer,1.004998638571319,neutral,0.5
+1177,3,377,1484958114,HFLevo,1.2965587422443934,neutral,nan
+1177,3,377,1484958114,LevoParadoxIsomer,0.4965587422443933,neutral,0.5
+1178,3,378,1217763652,HFLevo,1.4505600122642395,neutral,nan
+1178,3,378,1217763652,LevoParadoxIsomer,1.4505600122642395,neutral,0.19999995827674866
+1179,3,379,951904837,HFLevo,-0.6140963258839023,neutral,nan
+1179,3,379,951904837,LevoParadoxIsomer,0.18590367411609776,neutral,0.5
+1180,3,380,1611118613,HFLevo,1.5767451020137635,neutral,nan
+1180,3,380,1611118613,LevoParadoxIsomer,0.7767451020137636,neutral,0.5
+1181,3,381,518917746,HFLevo,-0.3309841660977714,neutral,nan
+1181,3,381,518917746,LevoParadoxIsomer,-0.3309841660977714,neutral,0.5
+1182,3,382,64896530,HFLevo,0.25041184278983375,neutral,nan
+1182,3,382,64896530,LevoParadoxIsomer,0.25041184278983375,neutral,0.34999996423721313
+1183,3,383,703323482,HFLevo,-0.4024154221788615,neutral,nan
+1183,3,383,703323482,LevoParadoxIsomer,-0.4024154221788615,neutral,0.5
+1184,3,384,878877531,HFLevo,0.3433688149110528,neutral,nan
+1184,3,384,878877531,LevoParadoxIsomer,-0.45663118508894723,neutral,0.5
+1185,3,385,743424612,HFLevo,1.3588170168722653,neutral,nan
+1185,3,385,743424612,LevoParadoxIsomer,1.3588170168722653,neutral,0.19999995827674866
+1186,3,386,1611600344,HFLevo,0.39585283780725167,neutral,nan
+1186,3,386,1611600344,LevoParadoxIsomer,1.1958528378072517,neutral,0.5
+1187,3,387,1653110007,HFLevo,1.505224568046769,neutral,nan
+1187,3,387,1653110007,LevoParadoxIsomer,1.505224568046769,neutral,0.7500000596046448
+1188,3,388,1477247030,HFLevo,-1.1364031988798702,neutral,nan
+1188,3,388,1477247030,LevoParadoxIsomer,0.06359680112012978,neutral,0.34999996423721313
+1189,3,389,460418126,HFLevo,0.5444399454267373,neutral,nan
+1189,3,389,460418126,LevoParadoxIsomer,2.5444399454267375,neutral,0.7500000596046448
+1190,3,390,965728184,HFLevo,-1.3056482391152837,neutral,nan
+1190,3,390,965728184,LevoParadoxIsomer,-0.5056482391152837,neutral,0.5
+1191,3,391,800112339,HFLevo,1.3941253259697812,neutral,nan
+1191,3,391,800112339,LevoParadoxIsomer,1.3941253259697812,neutral,0.5
+1192,3,392,1881978705,HFLevo,0.11355250158904308,neutral,nan
+1192,3,392,1881978705,LevoParadoxIsomer,0.11355250158904308,neutral,0.5
+1193,3,393,765013452,HFLevo,0.1964416250166414,neutral,nan
+1193,3,393,765013452,LevoParadoxIsomer,0.1964416250166414,neutral,0.5
+1194,3,394,1327556692,HFLevo,0.27807276108114626,neutral,nan
+1194,3,394,1327556692,LevoParadoxIsomer,0.27807276108114626,neutral,0.5
+1195,3,395,149816571,HFLevo,-0.2631924859672661,neutral,nan
+1195,3,395,149816571,LevoParadoxIsomer,0.536807514032734,neutral,0.5
+1196,3,396,338046121,HFLevo,2.3025212168232634,neutral,nan
+1196,3,396,338046121,LevoParadoxIsomer,2.3025212168232634,neutral,0.7500000596046448
+1197,3,397,994124776,HFLevo,0.38399826581662966,neutral,nan
+1197,3,397,994124776,LevoParadoxIsomer,0.38399826581662966,neutral,0.5
+1198,3,398,713057376,HFLevo,0.5402040989179696,neutral,nan
+1198,3,398,713057376,LevoParadoxIsomer,0.5402040989179696,neutral,0.5
+1199,3,399,1118891224,HFLevo,-0.48269914185478485,neutral,nan
+1199,3,399,1118891224,LevoParadoxIsomer,-0.48269914185478485,neutral,0.5
diff --git a/Down/run_all.bat b/Down/run_all.bat
new file mode 100644
index 0000000000000000000000000000000000000000..b21d325c4d9412a16487cee7d39955447c8bcc81
--- /dev/null
+++ b/Down/run_all.bat
@@ -0,0 +1,3 @@
+@echo off
+REM Run both DOWN and AntiDown experiments using current Python (Anaconda).
+python run_all.py
diff --git a/Down/run_all.py b/Down/run_all.py
new file mode 100644
index 0000000000000000000000000000000000000000..46b97d15b833d22f7c4c922883d21f789cdeb8d5
--- /dev/null
+++ b/Down/run_all.py
@@ -0,0 +1,67 @@
+"""Run both DOWN and AntiDown tabular experiments.
+
+Usage:
+  python run_all.py --episodes 400
+
+This will:
+- run down tabular paradox experiment
+- run AntiDown corrupted valley tabular experiment
+- generate plots for each into their graphics/ folders
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import subprocess
+from pathlib import Path
+import sys
+
+
+def _run(cmd: list[str], cwd: Path, env: dict[str, str] | None = None) -> None:
+    print(f"[run] ({cwd}) $ {' '.join(cmd)}")
+    merged = os.environ.copy()
+    if env:
+        merged.update(env)
+    subprocess.run(cmd, cwd=str(cwd), check=True, env=merged)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--episodes", type=int, default=400, help="Episodes per phase (or main loop) where applicable")
+    args = ap.parse_args()
+
+    root = Path(__file__).resolve().parent
+    results_dir = root / "results"
+    graphics_dir = root / "graphics"
+    results_dir.mkdir(parents=True, exist_ok=True)
+    graphics_dir.mkdir(parents=True, exist_ok=True)
+    common_env = {
+        "TWOQUARKS_RESULTS_DIR": results_dir.as_posix(),
+        "TWOQUARKS_GRAPHICS_DIR": graphics_dir.as_posix(),
+    }
+    down_dir = root / "down"
+    AntiDown_dir = root / "AntiDown"
+
+    # DOWN
+    down_script = down_dir / "exp" / "run_paradox_tabular.py"
+    _run([sys.executable, str(down_script)], cwd=down_dir, env=common_env)
+    plot_script = down_dir / "exp" / "plot_paradox_results.py"
+    _run([sys.executable, str(plot_script)], cwd=down_dir, env=common_env)
+    # AntiDown
+    AntiDown_script = AntiDown_dir / "exp" / "run_corrupted_valley_tabular.py"
+    _run([sys.executable, str(AntiDown_script)], cwd=AntiDown_dir, env=common_env)
+    AntiDown_plot = AntiDown_dir / "exp" / "plot_corrupted_valley_results.py"
+    _run([sys.executable, str(AntiDown_plot)], cwd=AntiDown_dir, env=common_env)
+
+
+    # Dual overlay plot (Down vs AntiDown)
+    dual_plot = root / "plot_dual_down_antidown.py"
+    if dual_plot.exists():
+        _run([sys.executable, str(dual_plot)], cwd=root, env=common_env)
+
+    print("\nDone. Results and plots saved into root/results and root/graphics.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Down/run_all.sh b/Down/run_all.sh
new file mode 100644
index 0000000000000000000000000000000000000000..71da0a462f563462690830fcf08c9c5f1260d29b
--- /dev/null
+++ b/Down/run_all.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+python3 run_all.py
diff --git a/README.md b/README.md
index 1b3cb64811982cd84d0f27772270dddf83db75ed..8dc96f04b1176f0fb1cce9315a6d462ebded8c58 100644
--- a/README.md
+++ b/README.md
@@ -1,22 +1,19 @@
----
-title: QuarksLab
-emoji: 🧪
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 6.3.0
-python_version: 3.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Lab
----
+# TwoQuarks — QuarksLab (Interactive)
 
-# QuarksLab — TwoQuarks Interactive Lab
+This is a Hugging Face Space intended to **run real, bounded experiments** from Three Quarks:
 
-Real, interactive experiments. Modify seed/episodes/params and run.
+- **DOWN / AntiDown** (tabular experiments)
+- **STRANGE / AntiStrange** (HypothesisLab swarm)
+- **CHARM** (Enchanted Valley + CharmField)
 
+## Run locally
 
----
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Deploy to Hugging Face Spaces
+
+Create a new Space (Gradio) and push this repository.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/Strange/AntiStrange/envs/__init__.py b/Strange/AntiStrange/envs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/AntiStrange/envs/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/AntiStrange/envs/antihypothesis_lab_env.py b/Strange/AntiStrange/envs/antihypothesis_lab_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c4018b457fcf4cae90348be1571dcf1452517a8
--- /dev/null
+++ b/Strange/AntiStrange/envs/antihypothesis_lab_env.py
@@ -0,0 +1,150 @@
+# ======================================================================
+# ANTI-HYPOTHESIS LAB ENV
+# Entorno diseñado para forzar exploración disruptiva (ANTI-STRANGE)
+# ======================================================================
+
+from dataclasses import dataclass
+import numpy as np
+
+@dataclass
+class AntiHypothesisState:
+    phase: int
+    progress: float
+    stability: float
+    latent_potential: float
+    last_action: int
+
+
+class AntiHypothesisLabEnv:
+    """
+    Variante del laboratorio diseñada para exponer el comportamiento del
+    agente ANTI-STRANGE: romper attractores y desbloquear recompensas
+    profundas mediante exploración insistente.
+    """
+
+    def __init__(
+        self,
+        max_steps: int = 50,
+        phase_drift_prob: float = 0.03,
+        seed: int = 0,
+        potential_threshold: float = 0.6,
+    ):
+        self.max_steps = max_steps
+        self.rng = np.random.default_rng(seed)
+        self.phase_drift_prob = phase_drift_prob
+        self.potential_threshold = potential_threshold
+        self.step_count = 0
+        self.state = None
+
+    @property
+    def n_actions(self):
+        return 4   # respeta el mismo espacio de acción
+
+    def reset(self):
+        self.step_count = 0
+        self.state = AntiHypothesisState(
+            phase=1,
+            progress=0.1,
+            stability=0.2,
+            latent_potential=0.0,
+            last_action=-1,
+        )
+        return self._encode_state(self.state)
+
+    def _encode_state(self, s):
+        p_bin  = int(np.clip(s.progress * 5, 0, 4))
+        s_bin  = int(np.clip(s.stability * 5, 0, 4))
+        pot_bin = int(np.clip(s.latent_potential * 3, 0, 2))
+        return s.phase * 75 + p_bin * 15 + s_bin * 3 + pot_bin
+
+    def _sample_next_phase(self, phase, potential):
+        if potential >= self.potential_threshold and phase < 2:
+            return 2
+        if self.rng.random() > self.phase_drift_prob:
+            return phase
+        delta = self.rng.integers(-1, 2)
+        return int(np.clip(phase + delta, 0, 2))
+
+    def step(self, action: int):
+        self.step_count += 1
+        s = self.state
+
+        phase = s.phase
+
+        dP = dS = dPot = 0.0
+
+        if phase == 0:
+            if action == 0:
+                dP, dS, r = 0.10, -0.03, 0.02
+                dPot = 0.08
+            elif action == 1:
+                dP, dS, r = 0.03, 0.02, 0.03
+                dPot = 0.01
+            elif action == 2:
+                dP, dS, r = 0.08, 0.00, 0.03
+                dPot = 0.06
+            else:
+                dP, dS, r = 0.02, 0.02, 0.02
+
+        elif phase == 1:
+            if action == 0:
+                dP, dS, r = 0.03, -0.04, 0.00
+                dPot = 0.06
+            elif action == 1:
+                dP, dS, r = 0.05, 0.06, 0.06
+                dPot = 0.01
+            elif action == 2:
+                dP, dS, r = 0.04, 0.03, 0.05
+                dPot = 0.05
+            else:
+                dP, dS, r = 0.06, 0.06, 0.08
+
+        else:
+            if action == 0:
+                dP, dS, r = 0.02, -0.05, 0.0
+                dPot = -0.02
+            elif action == 1:
+                dP, dS, r = 0.05, 0.05, 0.07
+                dPot = -0.01
+            elif action == 2:
+                dP, dS, r = 0.05, 0.03, 0.06
+                dPot = -0.005
+            else:
+                dP, dS, r = 0.08, 0.05, 0.12
+                dPot = -0.03
+
+        dP += self.rng.normal(0, 0.01)
+        dS += self.rng.normal(0, 0.01)
+
+        new_p = np.clip(s.progress + dP, 0, 1)
+        new_s = np.clip(s.stability + dS, 0, 1)
+        new_pot = np.clip(s.latent_potential + dPot, 0, 1)
+
+        reward = r + 0.35 * new_p + 0.30 * new_s
+
+        if action == s.last_action:
+            reward -= 0.02
+
+        if new_p < 0.15 and new_s < 0.15:
+            reward -= 0.08
+
+        next_phase = self._sample_next_phase(phase, new_pot)
+
+        self.state = AntiHypothesisState(
+            phase=next_phase,
+            progress=new_p,
+            stability=new_s,
+            latent_potential=new_pot,
+            last_action=action,
+        )
+
+        done = self.step_count >= self.max_steps
+
+        info = {
+            "phase": next_phase,
+            "progress": float(new_p),
+            "stability": float(new_s),
+            "latent_potential": float(new_pot),
+        }
+
+        return self._encode_state(self.state), float(reward), bool(done), info
diff --git a/Strange/AntiStrange/exp/__init__.py b/Strange/AntiStrange/exp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/AntiStrange/exp/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/AntiStrange/exp/plot_results.py b/Strange/AntiStrange/exp/plot_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..eed8b6fb93909b6760dd47ee6d24ccc19ba7dd0c
--- /dev/null
+++ b/Strange/AntiStrange/exp/plot_results.py
@@ -0,0 +1,64 @@
+
+import os
+import sys
+from pathlib import Path
+
+THIS_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = THIS_DIR.parent
+sys.path.append(str(PROJECT_ROOT))
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def plot_from_csv(csv_path: Path, out_dir: Path, prefix: str) -> None:
+    df = pd.read_csv(csv_path)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    # Episode return
+    if {"episode","reward"}.issubset(df.columns):
+        ep_ret = df.groupby("episode")["reward"].sum().reset_index()
+        plt.figure()
+        plt.plot(ep_ret["episode"], ep_ret["reward"])
+        plt.xlabel("episode")
+        plt.ylabel("episode_return")
+        plt.title(f"{prefix} episode return")
+        plt.tight_layout()
+        plt.savefig(out_dir / f"{prefix.lower()}_episode_return.png", dpi=160)
+        plt.close()
+
+    # Stability / diversity if present
+    for col in ["stability","diversity","phase","progress","w_t","H_swarm","S_t","T"]:
+        if col in df.columns:
+            series = df.groupby("episode")[col].mean().reset_index()
+            plt.figure()
+            plt.plot(series["episode"], series[col])
+            plt.xlabel("episode")
+            plt.ylabel(col)
+            plt.title(f"{prefix} mean {col}")
+            plt.tight_layout()
+            plt.savefig(out_dir / f"{prefix.lower()}_{col}.png", dpi=160)
+            plt.close()
+
+def main():
+    import argparse
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--csv", required=True, help="Path to results CSV (relative to project root is ok).")
+    ap.add_argument("--out", default="graphics", help="Output directory for plots (relative ok).")
+    ap.add_argument("--prefix", default="Strange", help="Plot filename prefix.")
+    args = ap.parse_args()
+
+    csv_path = Path(args.csv)
+    if not csv_path.is_absolute():
+        csv_path = (PROJECT_ROOT / csv_path).resolve()
+    out_dir = Path(args.out)
+    if not out_dir.is_absolute():
+        out_dir = (PROJECT_ROOT / out_dir).resolve()
+
+    if not csv_path.exists():
+        raise FileNotFoundError(f"CSV not found: {csv_path}")
+
+    plot_from_csv(csv_path, out_dir, args.prefix)
+    print(f"[ok] plots saved to: {out_dir}")
+
+if __name__ == "__main__":
+    main()
diff --git a/Strange/AntiStrange/exp/run_antistrange_hypothesis_lab.py b/Strange/AntiStrange/exp/run_antistrange_hypothesis_lab.py
new file mode 100644
index 0000000000000000000000000000000000000000..05cb1faf2f672574687b17a3e473ccabee88c9a8
--- /dev/null
+++ b/Strange/AntiStrange/exp/run_antistrange_hypothesis_lab.py
@@ -0,0 +1,79 @@
+import os
+import sys
+import csv
+import numpy as np
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.abspath(os.path.join(THIS_DIR, ".."))
+sys.path.append(PROJECT_ROOT)
+
+def _resolve_path(p: str) -> str:
+    """Resolve relative paths against PROJECT_ROOT for reproducible runs."""
+    if os.path.isabs(p):
+        return p
+    return os.path.abspath(os.path.join(PROJECT_ROOT, p))
+
+
+from envs.antihypothesis_lab_env import AntiHypothesisLabEnv
+from levo.antistrange_swarm import AntiStrangeConfig, AntiStrangeSwarm
+
+
+def run_antistrange(
+    n_episodes: int = 200,
+    max_steps: int = 50,
+    seed: int = 0,
+    out_path: str = "results/antistrange_hypothesis_lab.csv",
+):
+    # Entorno "territorio anti" 🤘
+    env = AntiHypothesisLabEnv(max_steps=max_steps, seed=seed)
+    out_path = _resolve_path(out_path)
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+
+    cfg = AntiStrangeConfig(n_actions=env.n_actions, seed=seed)
+    agent = AntiStrangeSwarm(cfg)
+
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+
+    fieldnames = [
+        "episode", "step", "reward",
+        "T", "H_swarm", "S_t", "diversity", "w_t",
+        "phase", "progress", "stability",
+        "latent_potential",  # NUEVO: clave para ver cuándo despierta el anti
+    ]
+
+    with open(out_path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+
+        for ep in range(n_episodes):
+            s = env.reset()
+            done = False
+            step = 0
+
+            while not done:
+                a, info = agent.act(s, step)
+                s_next, r, done, env_info = env.step(a)
+                agent.update(s, a, r, s_next, done)
+
+                row = {
+                    "episode": ep,
+                    "step": step,
+                    "reward": r,
+                    "T": info["T"],
+                    "H_swarm": info["H_swarm"],
+                    "S_t": info["S_t"],
+                    "diversity": info["diversity"],
+                    "w_t": info["w_t"],
+                    "phase": env_info["phase"],
+                    "progress": env_info["progress"],
+                    "stability": env_info["stability"],
+                    "latent_potential": env_info.get("latent_potential", 0.0),
+                }
+                writer.writerow(row)
+
+                s = s_next
+                step += 1
+
+
+if __name__ == "__main__":
+    run_antistrange()
diff --git a/Strange/AntiStrange/graphics/antistrange_H_swarm.png b/Strange/AntiStrange/graphics/antistrange_H_swarm.png
new file mode 100644
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diff --git a/Strange/AntiStrange/graphics/antistrange_S_t.png b/Strange/AntiStrange/graphics/antistrange_S_t.png
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diff --git a/Strange/AntiStrange/graphics/antistrange_T.png b/Strange/AntiStrange/graphics/antistrange_T.png
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diff --git a/Strange/AntiStrange/graphics/antistrange_diversity.png b/Strange/AntiStrange/graphics/antistrange_diversity.png
new file mode 100644
index 0000000000000000000000000000000000000000..79e8d3bbcafb42ba29667bcc8fb3bfb87a93da49
--- /dev/null
+++ b/Strange/AntiStrange/graphics/antistrange_diversity.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f12265dbea48068fe1ff60bfaeb03d565685e1524ae1b5afc1149bd193cdeee
+size 100160
diff --git a/Strange/AntiStrange/graphics/antistrange_episode_return.png b/Strange/AntiStrange/graphics/antistrange_episode_return.png
new file mode 100644
index 0000000000000000000000000000000000000000..c0e2be823dd3dc0d4c075efe2e9adc1f7f002a80
--- /dev/null
+++ b/Strange/AntiStrange/graphics/antistrange_episode_return.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:098227673e7d689a788a6980579e4fc6b125a2b9f09d1e4b9fe4a0e08037fe86
+size 117512
diff --git a/Strange/AntiStrange/graphics/antistrange_phase.png b/Strange/AntiStrange/graphics/antistrange_phase.png
new file mode 100644
index 0000000000000000000000000000000000000000..3cc470869ed5cc9b4d057d01119a9f88c8c53691
--- /dev/null
+++ b/Strange/AntiStrange/graphics/antistrange_phase.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:328d1f699b1192a127d92276c387c43428727240faee4eec2e254ae66c32ff8b
+size 108250
diff --git a/Strange/AntiStrange/graphics/antistrange_progress.png b/Strange/AntiStrange/graphics/antistrange_progress.png
new file mode 100644
index 0000000000000000000000000000000000000000..97d035a280955711c77d5a44cf2df1af727af5f4
--- /dev/null
+++ b/Strange/AntiStrange/graphics/antistrange_progress.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc71180e0ebb0bb68a6d20b70f20554cbba7bc050e2775abcbfab22f9d8f0502
+size 134112
diff --git a/Strange/AntiStrange/graphics/antistrange_stability.png b/Strange/AntiStrange/graphics/antistrange_stability.png
new file mode 100644
index 0000000000000000000000000000000000000000..01fcd4e75bfe0345371fccee2f375a49f7994be0
--- /dev/null
+++ b/Strange/AntiStrange/graphics/antistrange_stability.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:358f21748c0658311c470fd86e646db45578f1d0a2bcb9a65500aeb3ce1c9522
+size 121926
diff --git a/Strange/AntiStrange/graphics/antistrange_w_t.png b/Strange/AntiStrange/graphics/antistrange_w_t.png
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..d46d468f4873bea190afff54ce3f02e884e7cd4e
--- /dev/null
+++ b/Strange/AntiStrange/graphics/index.html
@@ -0,0 +1,147 @@
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+<title>CHARM • Graphics</title>
+<link rel="icon" href="/favicon.svg" type="image/svg+xml"/>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+:root{
+  --bg:#000; --fg:#fff; --muted:rgba(255,255,255,.62);
+  --card:rgba(255,255,255,.04); --stroke:rgba(255,255,255,.10); --glass:rgba(0,0,0,.38);
+}
+body{font-family:Inter,system-ui,Segoe UI,Roboto,Arial;background:var(--bg);color:var(--fg);overflow-x:hidden}
+#quantumField{position:fixed;inset:0;width:100vw;height:100vh;z-index:-10;background:#000}
+nav{position:fixed;top:0;left:0;right:0;z-index:1000;padding:14px 24px;background:var(--glass);backdrop-filter:blur(12px);border-bottom:1px solid rgba(255,255,255,.06)}
+.nav-inner{max-width:1200px;margin:0 auto;display:flex;align-items:center;gap:16px}
+.brand{font-weight:700;font-size:1.1rem}
+.nav-links{display:flex;gap:10px;margin-left:18px}
+.nav-links a{text-decoration:none;color:var(--fg);padding:8px 12px;border:1px solid rgba(255,255,255,.12);border-radius:999px;font-size:.9rem;opacity:.86;transition:.15s ease;white-space:nowrap}
+.nav-links a:hover{opacity:1;background:rgba(255,255,255,.06)}
+.spacer{flex:1}
+.menu-btn{width:40px;height:40px;display:flex;align-items:center;justify-content:center;border:1px solid rgba(255,255,255,.18);border-radius:12px;cursor:pointer;background:rgba(255,255,255,.02)}
+.menu-btn span{display:block;width:18px;height:2px;background:#fff;position:relative}
+.menu-btn span::before,.menu-btn span::after{content:"";position:absolute;left:0;width:18px;height:2px;background:#fff}
+.menu-btn span::before{top:-6px} .menu-btn span::after{top:6px}
+.menu{position:absolute;top:60px;right:24px;min-width:220px;padding:10px;border-radius:14px;background:var(--glass);backdrop-filter:blur(14px);border:1px solid rgba(255,255,255,.10);box-shadow:0 0 30px rgba(0,0,0,.6);display:none}
+.menu.open{display:block}
+.menu a{display:block;padding:10px 10px;border-radius:10px;text-decoration:none;color:var(--fg);opacity:.9}
+.menu a:hover{background:rgba(255,255,255,.06);opacity:1}
+.menu .sep{height:1px;background:rgba(255,255,255,.10);margin:8px 2px}
+.wrap{max-width:1100px;margin:0 auto;padding:110px 6vw 70px}
+.h{font-size:1.35rem;font-weight:650}
+.sub{color:var(--muted);margin-top:10px;line-height:1.6;max-width:980px}
+.viewer{margin-top:20px;display:grid;gap:14px;justify-items:center}
+img{width:100%;max-width:1000px;max-height:70vh;object-fit:contain;border-radius:14px;background:rgba(255,255,255,.03);border:1px solid rgba(255,255,255,.10)}
+.controls{display:flex;gap:10px;flex-wrap:wrap;justify-content:center}
+.btn{text-decoration:none;color:var(--fg);padding:10px 14px;border-radius:999px;border:1px solid rgba(255,255,255,.14);background:rgba(255,255,255,.03);opacity:.92;transition:.15s ease}
+.btn:hover{opacity:1;background:rgba(255,255,255,.07)}
+.kbd{font-family:ui-monospace,Menlo,Consolas,monospace;background:#15151b;border:1px solid #2a2a33;border-radius:6px;padding:2px 6px;font-size:.85em;color:var(--muted)}
+.cap{color:var(--muted);max-width:980px;line-height:1.6;text-align:center}
+.rule{border:0;border-top:1px solid rgba(255,255,255,.10);margin:22px 0}
+</style>
+</head>
+<body>
+<canvas id="quantumField"></canvas>
+
+<nav>
+  <div class="nav-inner">
+    <div class="brand">TwoQuarks</div>
+    <div class="nav-links">
+      <a href="../../../index.html#down">DOWN</a>
+      <a href="../../../index.html#strange">STRANGE</a>
+      <a href="../../../index.html#top">TOP</a>
+      <a href="../../../index.html#charm">CHARM</a>
+      <a href="../../../index.html#up">UP</a>
+      <a href="../../../index.html#bottom">BOTTOM</a>
+    </div>
+    <div class="spacer"></div>
+    <div class="menu-btn" id="menuBtn"><span></span></div>
+    <div class="menu" id="siteMenu">
+      <a href="../../../index.html#about">ABOUT</a>
+      <a href="../../../quarkslab.html">QuarksLab</a>
+      <div class="sep"></div>
+      <a href="../../../quarks/bottom/resume.pdf">Resume</a>
+      <a href="../../../summary.pdf">Summary</a>
+    </div>
+  </div>
+</nav>
+
+<div class="wrap">
+  <div class="h">CHARM · Visual Diagnostics</div>
+  <div class="sub">These figures show robustness to heavy noise, keeping exploration controlled and policies coherent.</div>
+
+  <div class="viewer">
+    <img id="img" src="../anticharm/graphics/figure_01.png" alt="figure"/>
+    <div class="controls">
+      <a class="btn" href="javascript:void(0)" onclick="prev()">← Previous</a>
+      <a class="btn" href="javascript:void(0)" onclick="next()">Next →</a>
+      <span class="kbd">←</span><span class="kbd">→</span>
+    </div>
+    <div class="cap" id="cap">Figure 01</div>
+  </div>
+
+  <hr class="rule"/>
+  <div class="sub"><strong>Interpretation</strong><br/>These figures show robustness to heavy noise, keeping exploration controlled and policies coherent.</div>
+</div>
+
+<script>
+
+const canvas = document.getElementById('quantumField');
+const ctx = canvas.getContext('2d');
+function resize(){
+  canvas.width = innerWidth;
+  canvas.height = innerHeight;
+}
+resize();
+addEventListener('resize', resize);
+
+const stars = Array.from({length: 650}, () => ({
+  x: (Math.random()-0.5) * canvas.width,
+  y: (Math.random()-0.5) * canvas.height,
+  z: Math.random() * canvas.width
+}));
+
+function animate(){
+  ctx.clearRect(0,0,canvas.width,canvas.height);
+  for(const s of stars){
+    s.z -= 2.2;
+    if (s.z < 1){
+      s.x = (Math.random()-0.7) * canvas.width;
+      s.y = (Math.random()-0.7) * canvas.height;
+      s.z = canvas.width;
+    }
+    const k = 128 / s.z;
+    const x = s.x * k + canvas.width/2;
+    const y = s.y * k + canvas.height/2;
+    const r = (1 - s.z/canvas.width) * 1.2;
+    ctx.beginPath();
+    ctx.fillStyle = "rgba(140,180,255,0.85)";
+    ctx.arc(x,y,r,0,Math.PI*2);
+    ctx.fill();
+  }
+  requestAnimationFrame(animate);
+}
+animate();
+
+</script>
+<script>
+const menuBtn = document.getElementById('menuBtn');
+const siteMenu = document.getElementById('siteMenu');
+menuBtn.addEventListener('click', (e)=>{
+  e.stopPropagation(); siteMenu.classList.toggle('open');
+});
+document.addEventListener('click', ()=> siteMenu.classList.remove('open'));
+siteMenu.addEventListener('click', (e)=> e.stopPropagation());
+
+const images = [{"src": "anticharm/graphics/figure_01.png", "text": "Figure 01"}, {"src": "anticharm/graphics/figure_02.png", "text": "Figure 02"}, {"src": "anticharm/graphics/figure_03.png", "text": "Figure 03"}, {"src": "charm/graphics/figure_01.png", "text": "Figure 04"}, {"src": "charm/graphics/figure_02.png", "text": "Figure 05"}, {"src": "charm/graphics/figure_03.png", "text": "Figure 06"}, {"src": "freq_policies/graphics/figure_01.png", "text": "Figure 07"}, {"src": "freq_policies/graphics/figure_02.png", "text": "Figure 08"}, {"src": "freq_policies/graphics/figure_03.png", "text": "Figure 09"}, {"src": "z_olo/graphics/figure_01.png", "text": "Figure 10"}, {"src": "z_olo/graphics/figure_02.png", "text": "Figure 11"}, {"src": "z_olo/graphics/figure_03.png", "text": "Figure 12"}];
+let i = 0;
+function show(){ document.getElementById("img").src = images[i].src; document.getElementById("cap").textContent = images[i].text; }
+function next(){ i=(i+1)%images.length; show(); }
+function prev(){ i=(i-1+images.length)%images.length; show(); }
+document.addEventListener("keydown",(e)=>{ if(e.key==="ArrowRight") next(); if(e.key==="ArrowLeft") prev(); });
+</script>
+</body>
+</html>
diff --git a/Strange/AntiStrange/levo/__init__.py b/Strange/AntiStrange/levo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/AntiStrange/levo/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/AntiStrange/levo/antistrange_swarm.py b/Strange/AntiStrange/levo/antistrange_swarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff65a2553aee9960d04b4a8379408e426207ab0b
--- /dev/null
+++ b/Strange/AntiStrange/levo/antistrange_swarm.py
@@ -0,0 +1,359 @@
+"""
+ANTI-STRANGE Swarm
+------------------
+Contraparte "antiquark" del hadrón STRANGE.
+
+Diferencias conceptuales con StrangeSwarm:
+
+- Misma base HF-Levo tabular, pero:
+  * Termodinámica responde distinto a ΔR (premia mejoras fuertes).
+  * Gate w_t se activa cuando el sistema está demasiado ordenado.
+  * Usa una memoria *repulsora*: anti-proto, que empuja a acciones
+    históricamente poco visitadas (huecos del mapa de políticas).
+
+- Interfaz compatible con StrangeSwarm:
+    AntiStrangeConfig, AntiStrangeSwarm.act(), AntiStrangeSwarm.update().
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Hashable
+import numpy as np
+import math
+
+
+# ===================== HF-Levo agent (copiado para independencia) =====================
+
+@dataclass
+class HFLevoAgent:
+    n_actions: int
+    alpha: float = 0.1
+    gamma: float = 0.99
+    tau_base: float = 1.0
+
+    def __post_init__(self):
+        # Q: dict[state][action] -> value
+        self.Q: Dict[Hashable, np.ndarray] = {}
+        # Fase fija por acción para inducir diversidad real entre acciones
+        self.action_phases = np.linspace(
+            0.0, 2 * math.pi, self.n_actions, endpoint=False
+        )
+
+    def _ensure_state(self, s: Hashable):
+        if s not in self.Q:
+            self.Q[s] = np.zeros(self.n_actions, dtype=float)
+
+    def policy(
+        self,
+        s: Hashable,
+        t: int,
+        T: float,
+        hf_amp: float,
+        hf_omega: float,
+        phase_offset: float,
+        rng: np.random.Generator,
+    ) -> np.ndarray:
+        """
+        HF-Levo:
+            scores = Q + A(T) * cos(w(T)*t + phi_h,a)
+            p ~ softmax(scores / tau(T))
+        """
+        self._ensure_state(s)
+        q = self.Q[s]
+
+        # temperatura de softmax: tau sube con T (más exploración)
+        tau = self.tau_base * (1.0 + 0.5 * T)
+
+        # oscilación por acción: desfase del agente + desfase propio por acción
+        osc_vec = hf_amp * np.cos(
+            hf_omega * t + phase_offset + self.action_phases
+        )
+        scores = q + osc_vec
+
+        # softmax numéricamente estable
+        z = scores - scores.max()
+        exp_z = np.exp(z / max(1e-8, tau))
+        p = exp_z / exp_z.sum()
+        return p
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        self._ensure_state(s)
+        self._ensure_state(s_next)
+        q = self.Q[s]
+        q_next = self.Q[s_next]
+        target = r if done else (r + self.gamma * float(q_next.max()))
+        td = target - q[a]
+        q[a] += self.alpha * td
+
+
+# ===================== Config ANTI-STRANGE =====================
+
+@dataclass
+class AntiStrangeConfig:
+    n_agents: int = 5
+    n_actions: int = 4
+    hf_amp_base: float = 0.15
+    hf_omega_base: float = 0.35
+
+    T_init: float = 0.7
+    T_min: float = 0.0
+    T_max: float = 2.0
+    eta_T: float = 0.05  # pasos de T
+
+    # --- Excitación (sube T) ---
+    # Queremos subir T cuando:
+    # - Hay mejoras fuertes (|ΔR| grande positiva).
+    # - El sistema está demasiado ordenado (baja entropía y baja diversidad).
+    eps_R_pos: float = 0.03    # mejora significativa de recompensa
+    H_low: float = 0.7         # entropía baja
+    D_low: float = 0.08        # diversidad baja
+
+    # --- Regulación (baja T) ---
+    # Queremos bajar T cuando:
+    # - El sistema se vuelve caótico (H o diversidad altas).
+    # - Hay caídas fuertes de recompensa.
+    H_high: float = 1.25       # entropía muy alta (~max)
+    D_high: float = 0.20       # diversidad muy alta
+    eps_crash: float = 0.03    # caída fuerte de reward
+
+    # --- Gate memoria repulsora ---
+    # Gate alto cuando el sistema está demasiado "congelado":
+    # T baja, S_t baja, H_swarm baja.
+    T_mid: float = 0.9
+    S_mid: float = 0.06
+    H_mid: float = 0.8
+
+    kT: float = 1.5
+    kS: float = 2.0
+    kH: float = 1.0
+
+    proto_alpha: float = 0.1
+
+    seed: int = 1  # distinto de Strange por defecto
+
+
+# ===================== ANTI-STRANGE Swarm =====================
+
+class AntiStrangeSwarm:
+    """
+    Hadron ANTI-STRANGE.
+
+    Diferencias clave vs StrangeSwarm:
+      - T sube cuando hay mejoras fuertes y orden excesivo.
+      - Gate w_t empuja a memoria REPULSORA (anti-proto).
+      - Diseñado para explorar huecos de la política del enjambre.
+
+    Interfaz:
+        anti = AntiStrangeSwarm(cfg)
+        s = env.reset()
+        for t in ...:
+            a, info = anti.act(s, t)
+            s_next, r, done, env_info = env.step(a)
+            anti.update(s, a, r, s_next, done)
+    """
+
+    def __init__(self, config: AntiStrangeConfig):
+        self.cfg = config
+        self.n_actions = config.n_actions
+        self.agents: List[HFLevoAgent] = [
+            HFLevoAgent(n_actions=config.n_actions)
+            for _ in range(config.n_agents)
+        ]
+        self.rng = np.random.default_rng(config.seed)
+
+        # estado interno
+        self.T = config.T_init
+        self.proto: Dict[Hashable, np.ndarray] = {}
+
+        self.last_reward = 0.0
+        self.last_delta_R = 0.0
+
+        # fases diferentes para cada agente
+        self.agent_phases = np.linspace(
+            0.0, 2 * math.pi, config.n_agents, endpoint=False
+        )
+
+    # --------- Métricas internas ---------
+    @staticmethod
+    def _entropy(p: np.ndarray) -> float:
+        p_safe = np.clip(p, 1e-8, 1.0)
+        return float(-(p_safe * np.log(p_safe)).sum())
+
+    @staticmethod
+    def _diversity(ps: np.ndarray) -> float:
+        H, A = ps.shape
+        if H <= 1:
+            return 0.0
+        d = 0.0
+        cnt = 0
+        for i in range(H):
+            for j in range(i + 1, H):
+                d += float(np.abs(ps[i] - ps[j]).mean())
+                cnt += 1
+        return d / max(1, cnt)
+
+    # --------- Core swarm policy ---------
+    def _compute_swarm_policy(self, s: Hashable, t: int):
+        cfg = self.cfg
+        ps = []
+        for h, agent in enumerate(self.agents):
+            phase = self.agent_phases[h]
+            p_h = agent.policy(
+                s,
+                t,
+                self.T,
+                hf_amp=cfg.hf_amp_base * (1.0 + 0.5 * self.T),
+                hf_omega=cfg.hf_omega_base * (1.0 + 0.25 * self.T),
+                phase_offset=phase,
+                rng=self.rng,
+            )
+            ps.append(p_h)
+        ps = np.stack(ps)  # [H, A]
+        p_swarm = ps.mean(axis=0)
+        var = ps.var(axis=0)
+        S_t = float(var.mean())
+        H_swarm = self._entropy(p_swarm)
+        diversity = self._diversity(ps)
+        return p_swarm, S_t, H_swarm, diversity
+
+    def _update_proto(self, s: Hashable, p_swarm: np.ndarray):
+        cfg = self.cfg
+        if s not in self.proto:
+            self.proto[s] = p_swarm.copy()
+        else:
+            self.proto[s] = (
+                (1.0 - cfg.proto_alpha) * self.proto[s]
+                + cfg.proto_alpha * p_swarm
+            )
+
+    def _anti_proto(self, s: Hashable) -> np.ndarray:
+        """
+        Construye memoria repulsora:
+            anti_proto ∝ 1 - proto
+        Es decir, favorece acciones históricamente poco usadas.
+        """
+        proto_s = self.proto[s]
+        raw = 1.0 - proto_s
+        raw = np.clip(raw, 1e-8, None)
+        return raw / raw.sum()
+
+    def _thermo_step(
+        self,
+        delta_R: float,
+        H_swarm: float,
+        S_t: float,
+        diversity: float,
+    ):
+        """
+        Control térmico "anti":
+
+        - E_t (excitación, sube T):
+            * |ΔR| grande positiva (mejora fuerte de recompensa).
+            * Orden excesivo: H_swarm baja y diversidad baja.
+
+        - R_t (regulación, baja T):
+            * Caos fuerte: H_swarm muy alta o diversidad muy alta.
+            * Crash de recompensa: ΔR muy negativa.
+        """
+        cfg = self.cfg
+
+        improving = delta_R > cfg.eps_R_pos
+        too_ordered = (H_swarm < cfg.H_low) and (diversity < cfg.D_low)
+
+        E_t = (
+            (1.0 if improving else 0.0) +
+            (1.0 if too_ordered else 0.0)
+        )
+
+        too_chaotic = (H_swarm > cfg.H_high) or (diversity > cfg.D_high)
+        crashing = delta_R < -cfg.eps_crash
+
+        R_t = (
+            (1.0 if too_chaotic else 0.0) +
+            (1.0 if crashing else 0.0)
+        )
+
+        self.T += cfg.eta_T * (E_t - R_t)
+        self.T = float(np.clip(self.T, cfg.T_min, cfg.T_max))
+        return float(E_t), float(R_t)
+
+    def _gate(self, H_swarm: float, S_t: float) -> float:
+        """
+        Gate invertido:
+
+        - Queremos w_t alto cuando el sistema está demasiado
+          ordenado y frío (baja entropía / baja varianza / T baja),
+          para empujar hacia acciones en los "huecos" (anti-proto).
+
+        - Cuando T, S_t y H_swarm son altos, w_t tiende a 0
+          y se deja actuar más a p_swarm.
+        """
+        cfg = self.cfg
+        x = (
+            cfg.kT * (cfg.T_mid - self.T) +
+            cfg.kS * (cfg.S_mid - S_t) +
+            cfg.kH * (cfg.H_mid - H_swarm)
+        )
+        w = 1.0 / (1.0 + math.exp(-x))
+        return float(w)
+
+    # --------- Interfaz pública ---------
+    def act(self, s: Hashable, t: int):
+        """
+        Calcula la política ANTI-STRANGE y samplea una acción.
+        Devuelve:
+            action, info_dict
+        """
+        p_swarm, S_t, H_swarm, diversity = self._compute_swarm_policy(s, t)
+        self._update_proto(s, p_swarm)
+
+        # feedback térmico con ΔR real del paso previo
+        delta_R = self.last_delta_R
+        E_t, R_t = self._thermo_step(delta_R, H_swarm, S_t, diversity)
+
+        # memoria repulsora
+        anti_proto_s = self._anti_proto(s)
+        w_t = self._gate(H_swarm, S_t)
+
+        p_final = (1.0 - w_t) * p_swarm + w_t * anti_proto_s
+        p_final = p_final / p_final.sum()
+
+        a = int(self.rng.choice(len(p_final), p=p_final))
+
+        info = {
+            "T": self.T,
+            "S_t": S_t,
+            "H_swarm": H_swarm,
+            "diversity": diversity,
+            "E_t": E_t,
+            "R_t": R_t,
+            "w_t": w_t,
+            "p_swarm": p_swarm,
+            "p_final": p_final,
+        }
+        return a, info
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        # Actualizamos Q de todos los agentes
+        for agent in self.agents:
+            agent.update(s, a, r, s_next, done)
+
+        # ΔR real
+        delta_R = r - self.last_reward
+        self.last_reward = r
+        self.last_delta_R = delta_R
+
+        return delta_R
diff --git a/Strange/AntiStrange/results/antistrange_hypothesis_lab.csv b/Strange/AntiStrange/results/antistrange_hypothesis_lab.csv
new file mode 100644
index 0000000000000000000000000000000000000000..789b1a2f9b406ccc4df86a074d477a15d39e5a4f
--- /dev/null
+++ b/Strange/AntiStrange/results/antistrange_hypothesis_lab.csv
@@ -0,0 +1,10001 @@
+episode,step,reward,T,H_swarm,S_t,diversity,w_t,phase,progress,stability,latent_potential
+0,0,0.168043741183953,0.6499999999999999,1.3862943611198846,0.0007001722195526522,0.03633489525742447,0.47684290231339843,1,0.14125730221093394,0.228678951367087,0.05
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diff --git a/Strange/AntiStrange/results/strange_hypothesislab.csv b/Strange/AntiStrange/results/strange_hypothesislab.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5033653d4603c4e2822e0291fafc2d9bfae04ed1
--- /dev/null
+++ b/Strange/AntiStrange/results/strange_hypothesislab.csv
@@ -0,0 +1,10001 @@
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diff --git a/Strange/AntiStrange/results/train_metrics.csv b/Strange/AntiStrange/results/train_metrics.csv
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diff --git a/Strange/MODEL.md b/Strange/MODEL.md
new file mode 100644
index 0000000000000000000000000000000000000000..a979e78b12892d5ba5521b70d72b7b94080488ad
--- /dev/null
+++ b/Strange/MODEL.md
@@ -0,0 +1,88 @@
+\# Model Description: Strange / AntiStrange
+
+
+
+\## Purpose
+
+
+
+Strange / AntiStrange are reinforcement learning agents designed to study failure under hidden regime shifts and phase transitions. The focus is on internal instability rather than external performance.
+
+
+
+\## Strange Agent
+
+
+
+\*\*Role:\*\*  
+
+Strange represents a standard learner assuming environmental stationarity.
+
+
+
+\*\*Key Properties:\*\*
+
+\- Performs well under initial regimes
+
+\- Fails abruptly when latent dynamics shift
+
+\- Continues acting confidently despite invalid assumptions
+
+
+
+\## AntiStrange Agent
+
+
+
+\*\*Role:\*\*  
+
+AntiStrange is designed as a counterfactual probe with altered sensitivity to regime inconsistency.
+
+
+
+\*\*Key Properties:\*\*
+
+\- Detects instability earlier
+
+\- Deviates sooner from established policies
+
+\- Enables comparative analysis of collapse timing
+
+
+
+\## Dual Hypothesis Environment
+
+
+
+The `dual\_hypothesis\_lab\_env` exposes both agents to identical observations while enforcing hidden regime changes. This enables direct comparison of:
+
+\- Adaptation lag
+
+\- Behavioral divergence
+
+\- Collapse signatures
+
+
+
+\## Safety
+
+
+
+These models formalize a critical safety risk:
+
+> Systems that fail not because of noise, but because the world quietly changes.
+
+
+
+They are intended as diagnostic tools for studying early-warning signals and regime-aware control strategies.
+
+
+
+\## Notes
+
+
+
+The implementation prioritizes clarity and inspectability over complexity to support safety-focused analysis and experimentation.
+
+
+
diff --git a/Strange/README.md b/Strange/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..41f7cae99dd9d494693ed67dc07f22a676b4eaa3
--- /dev/null
+++ b/Strange/README.md
@@ -0,0 +1,116 @@
+\# Phase Shift \& Regime Uncertainty  
+
+\## Strange / AntiStrange
+
+
+
+This project studies reinforcement learning systems operating under hidden regime shifts and non-stationary dynamics. It focuses on how agents fail when the environment changes phase without explicit signaling, a scenario common in real-world deployment and safety-critical systems.
+
+
+
+\## Core Idea
+
+
+
+Many environments appear stationary until they are not. This project explores:
+
+\- Latent phase transitions
+
+\- Sudden behavioral collapse despite stable metrics
+
+\- The gap between observed performance and underlying stability
+
+
+
+Strange / AntiStrange are paired agents designed to probe these dynamics from different behavioral assumptions.
+
+
+
+
+
+\## Project Structure
+
+
+
+\- `agents/`  
+
+&nbsp; #Strange and AntiStrange agent implementations.
+
+
+
+\- `envs/`  
+
+&nbsp; #Phase-shifting environments, including `dual\_hypothesis\_lab\_env`.
+
+
+
+\- `run\_all.py`  
+
+&nbsp; #Executes both agents in a shared environment for direct comparison.
+
+
+
+\- `results/`  
+
+&nbsp; #CSV logs capturing regime transitions and performance degradation.
+
+
+
+\- `graphics/`  
+
+&nbsp; #Comparative plots showing divergence under regime shifts.
+
+
+
+
+
+\## Running the Experiments
+
+
+
+This project is designed to run without notebooks. (I resorted to Anaconda)
+
+
+
+```bash
+
+conda activate your\_env
+
+python run\_all.py
+
+
+
+\## Safety 
+
+\## Why This Matters for AI Safety
+
+
+
+-Hidden regime shifts are a major source of real-world AI failures. 
+
+-This project operationalizes questions such as:
+
+
+
+&nbsp; -How long can agents operate under false assumptions?
+
+
+
+&nbsp; -What does “pre-collapse” behavior look like?
+
+
+
+&nbsp; -Can instability be detected before performance drops?
+
+
+
+\## Related Work
+
+
+
+-This project accompanies the preprint:
+
+-Adaptive Stability Control in Sequential Learning Systems
+
+
+
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diff --git a/Strange/graphics/index.html b/Strange/graphics/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..762b620473543abf03e956795a811a91238c9342
--- /dev/null
+++ b/Strange/graphics/index.html
@@ -0,0 +1,245 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+<title>STRANGE • Graphics</title>
+<link rel="icon" href="/favicon.svg" type="image/svg+xml"/>
+
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+:root{
+  --bg:#000; --fg:#fff; --muted:rgba(255,255,255,.62);
+  --card:rgba(255,255,255,.04); --stroke:rgba(255,255,255,.10); --glass:rgba(0,0,0,.38);
+  --accent:#8cb4ff;
+}
+body{font-family:Inter,system-ui,Segoe UI,Roboto,Arial;background:var(--bg);color:var(--fg);overflow-x:hidden}
+#quantumField{position:fixed;inset:0;width:100vw;height:100vh;z-index:-10;background:#000}
+
+nav{position:fixed;top:0;left:0;right:0;z-index:1000;padding:14px 24px;background:var(--glass);backdrop-filter:blur(12px);border-bottom:1px solid rgba(255,255,255,.06)}
+.nav-inner{max-width:1200px;margin:0 auto;display:flex;align-items:center;gap:16px}
+.brand{font-weight:700;font-size:1.1rem}
+.nav-links{display:flex;gap:10px;margin-left:18px}
+.nav-links a{text-decoration:none;color:var(--fg);padding:8px 12px;border:1px solid rgba(255,255,255,.12);border-radius:999px;font-size:.9rem;opacity:.86;transition:.15s ease;white-space:nowrap}
+.nav-links a:hover{opacity:1;background:rgba(255,255,255,.06)}
+.spacer{flex:1}
+.menu-btn{width:40px;height:40px;display:flex;align-items:center;justify-content:center;border:1px solid rgba(255,255,255,.18);border-radius:12px;cursor:pointer;background:rgba(255,255,255,.02)}
+.menu-btn span{display:block;width:18px;height:2px;background:#fff;position:relative}
+.menu-btn span::before,.menu-btn span::after{content:"";position:absolute;left:0;width:18px;height:2px;background:#fff}
+.menu-btn span::before{top:-6px} .menu-btn span::after{top:6px}
+.menu{position:absolute;top:60px;right:24px;min-width:220px;padding:10px;border-radius:14px;background:var(--glass);backdrop-filter:blur(14px);border:1px solid rgba(255,255,255,.10);box-shadow:0 0 30px rgba(0,0,0,.6);display:none}
+.menu.open{display:block}
+.menu a{display:block;padding:10px 10px;border-radius:10px;text-decoration:none;color:var(--fg);opacity:.9}
+.menu a:hover{background:rgba(255,255,255,.06);opacity:1}
+.menu .sep{height:1px;background:rgba(255,255,255,.10);margin:8px 2px}
+
+.wrap{max-width:1100px;margin:0 auto;padding:110px 6vw 70px}
+.h{font-size:1.35rem;font-weight:650}
+.sub{color:var(--muted);margin-top:10px;line-height:1.6;max-width:980px}
+
+.viewer{margin-top:20px;display:grid;gap:18px;justify-items:center}
+
+/* 15% smaller */
+img{
+  width:85%;
+  max-width:850px;
+  max-height:60vh;
+  object-fit:contain;
+  border-radius:14px;
+  background:rgba(255,255,255,.03);
+  border:1px solid rgba(255,255,255,.10)
+}
+
+/* controls: centered + cleaner spacing */
+.controls{
+  display:flex;
+  justify-content:center;
+  align-items:center;
+  gap:18px;
+  margin-top:6px;
+}
+
+.btn{text-decoration:none;color:var(--fg);padding:10px 14px;border-radius:999px;border:1px solid rgba(255,255,255,.14);background:rgba(255,255,255,.03);opacity:.92;transition:.15s ease}
+.btn:hover{opacity:1;background:rgba(255,255,255,.07)}
+
+.kbd{font-family:ui-monospace,Menlo,Consolas,monospace;background:#15151b;border:1px solid #2a2a33;border-radius:6px;padding:2px 6px;font-size:.85em;color:var(--muted)}
+
+.cap{color:var(--muted);max-width:980px;line-height:1.55;text-align:center}
+.cap strong{color:rgba(255,255,255,.9)}
+.rule{border:0;border-top:1px solid rgba(255,255,255,.10);margin:22px 0}
+.interp{color:var(--muted);max-width:980px;line-height:1.65}
+</style>
+</head>
+
+<body>
+<canvas id="quantumField"></canvas>
+
+<nav>
+  <div class="nav-inner">
+    <div class="brand">TwoQuarks</div>
+    <div class="nav-links">
+      <a href="../../../index.html#down">DOWN</a>
+      <a href="../../../index.html#strange">STRANGE</a>
+      <a href="../../../index.html#top">TOP</a>
+      <a href="../../../index.html#charm">CHARM</a>
+      <a href="../../../index.html#up">UP</a>
+      <a href="../../../index.html#bottom">BOTTOM</a>
+    </div>
+    <div class="spacer"></div>
+    <div class="menu-btn" id="menuBtn"><span></span></div>
+    <div class="menu" id="siteMenu">
+      <a href="../../../index.html#about">ABOUT</a>
+      <a href="../../../quarkslab.html">QuarksLab</a>
+      <div class="sep"></div>
+      <a href="../../../quarks/bottom/resume.pdf">Resume</a>
+      <a href="../../../summary.pdf">Summary</a>
+    </div>
+  </div>
+</nav>
+
+<div class="wrap">
+  <div class="h">STRANGE · Visual Diagnostics</div>
+  <div class="sub">
+    Use keyboard arrows or the buttons below to navigate.
+  </div>
+
+  <div class="viewer">
+    <img id="img" src="graphics/strange_episode_return.png" alt="figure"/>
+    <div class="controls">
+      <a class="btn" href="javascript:void(0)" onclick="prev()">← Previous</a>
+      <a class="btn" href="javascript:void(0)" onclick="next()">Next →</a>
+    </div>
+
+    <div class="cap" id="cap"><strong>Strange</strong> — Episode Return</div>
+  </div>
+
+  <hr class="rule"/>
+
+  <div class="interp" id="interp">
+    Interpretation will appear here.
+  </div>
+</div>
+
+<!-- STARFIELD -->
+<script>
+const canvas = document.getElementById('quantumField');
+const ctx = canvas.getContext('2d');
+function resize(){ canvas.width = innerWidth; canvas.height = innerHeight; }
+resize(); addEventListener('resize', resize);
+
+const stars = Array.from({length: 650}, () => ({
+  x: (Math.random()-0.5) * canvas.width,
+  y: (Math.random()-0.5) * canvas.height,
+  z: Math.random() * canvas.width
+}));
+
+function animate(){
+  ctx.clearRect(0,0,canvas.width,canvas.height);
+  for(const s of stars){
+    s.z -= 2.2;
+    if (s.z < 1){
+      s.x = (Math.random()-0.7) * canvas.width;
+      s.y = (Math.random()-0.7) * canvas.height;
+      s.z = canvas.width;
+    }
+    const k = 128 / s.z;
+    const x = s.x * k + canvas.width/2;
+    const y = s.y * k + canvas.height/2;
+    const r = (1 - s.z/canvas.width) * 1.2;
+    ctx.beginPath();
+    ctx.fillStyle = "rgba(140,180,255,0.85)";
+    ctx.arc(x,y,r,0,Math.PI*2);
+    ctx.fill();
+  }
+  requestAnimationFrame(animate);
+}
+animate();
+</script>
+
+<!-- MENU + VIEWER -->
+<script>
+const menuBtn = document.getElementById('menuBtn');
+const siteMenu = document.getElementById('siteMenu');
+if(menuBtn && siteMenu){
+  menuBtn.addEventListener('click', (e)=>{ e.stopPropagation(); siteMenu.classList.toggle('open'); });
+  document.addEventListener('click', ()=> siteMenu.classList.remove('open'));
+  siteMenu.addEventListener('click', (e)=> e.stopPropagation());
+}
+
+/*
+  Assumption:
+  index.html is in the model folder and PNGs are in ./graphics/
+*/
+
+const figures = [
+  // --- STRANGE ---
+  { src:"graphics/strange_episode_return.png", title:"Strange — Episode Return",
+    interpretation:"Episode-level return over time. Look for trend (improving/flat/declining) and variability (stable band vs frequent deep drops)." },
+  { src:"graphics/strange_stability.png", title:"Strange — Stability",
+    interpretation:"A stability score across episodes. Higher/steadier values indicate consistent behavior; sharp oscillations suggest regime switching or instability." },
+  { src:"graphics/strange_diversity.png", title:"Strange — Diversity",
+    interpretation:"A diversity / exploration footprint. Decreasing diversity can indicate convergence; persistently high diversity can indicate continued exploration or thrashing." },
+  { src:"graphics/strange_progress.png", title:"Strange — Progress",
+    interpretation:"A progress proxy (learning signal). Saturation can indicate convergence or a plateau; strong oscillations suggest sensitivity to noise or non-stationarity." },
+  { src:"graphics/strange_phase.png", title:"Strange — Phase",
+    interpretation:"A phase/regime indicator. Stable bands imply consistent mode; frequent jumps imply switching between behaviors or unstable dynamics." },
+  { src:"graphics/strange_H_swarm.png", title:"Strange — H_swarm",
+    interpretation:"An entropy/dispersion-like signal. A gradual decrease often corresponds to consolidation; persistent elevation suggests diffuse behavior." },
+  { src:"graphics/strange_S_t.png", title:"Strange — S_t",
+    interpretation:"Tracked signal S_t over time. Bounded drift suggests controlled dynamics; sudden spikes/drops can indicate shocks or measurement sensitivity." },
+  { src:"graphics/strange_T.png", title:"Strange — T",
+    interpretation:"Temporal/transition signal T. Spikes typically correspond to abrupt changes; smoother traces indicate gradual adaptation." },
+  { src:"graphics/strange_w_t.png", title:"Strange — w_t",
+    interpretation:"Parameter/weight trace w_t. Slow drift suggests stable optimization; erratic movement or jumps can indicate unstable updates or scaling issues." },
+
+  // --- ANTISTRANGE ---
+  { src:"graphics/antistrange_episode_return.png", title:"AntiStrange — Episode Return",
+    interpretation:"Episode return for the AntiStrange variant. Compare trend and variance versus Strange to see symmetry/asymmetry in learning dynamics." },
+  { src:"graphics/antistrange_stability.png", title:"AntiStrange — Stability",
+    interpretation:"Stability score for AntiStrange. Differences relative to Strange highlight how robust/fragile the dynamics are under the anti-variant." },
+  { src:"graphics/antistrange_diversity.png", title:"AntiStrange — Diversity",
+    interpretation:"Exploration diversity for AntiStrange. High diversity with flat returns can indicate diffuse search; low diversity with flat returns can indicate getting stuck." },
+  { src:"graphics/antistrange_progress.png", title:"AntiStrange — Progress",
+    interpretation:"Progress proxy for AntiStrange. Divergence between progress and return can indicate proxy optimization (improving metric without return gains)." },
+  { src:"graphics/antistrange_phase.png", title:"AntiStrange — Phase",
+    interpretation:"Phase/regime indicator for AntiStrange. Frequent jumps suggest unstable mode switching; stable ranges suggest a consistent operating regime." },
+  { src:"graphics/antistrange_H_swarm.png", title:"AntiStrange — H_swarm",
+    interpretation:"Entropy/dispersion-like signal for AntiStrange. Compare with Strange to see whether the anti-variant consolidates or remains diffuse." },
+  { src:"graphics/antistrange_S_t.png", title:"AntiStrange — S_t",
+    interpretation:"Tracked signal S_t for AntiStrange. Large excursions versus Strange can indicate stronger sensitivity or less damping." },
+  { src:"graphics/antistrange_T.png", title:"AntiStrange — T",
+    interpretation:"Temporal/transition signal T for AntiStrange. Spikes reflect abrupt transitions; a late rising band can indicate increasing volatility." },
+  { src:"graphics/antistrange_w_t.png", title:"AntiStrange — w_t",
+    interpretation:"Parameter/weight trace w_t for AntiStrange. More jitter or drift indicates less stable optimization relative to Strange." },
+
+  // --- DUAL ---
+  { src:"graphics/dual_episode_return.png", title:"Dual — Episode Return (Strange vs AntiStrange)",
+    interpretation:"Overlay of episode returns for both variants. A persistent gap suggests asymmetric performance; repeated crossings suggest similar behavior under noise." },
+  { src:"graphics/dual_stability.png", title:"Dual — Stability (Strange vs AntiStrange)",
+    interpretation:"Overlay of stability scores. Separation indicates differing robustness; similar bands indicate comparable stability under the same conditions." }
+];
+
+let idx = 0;
+
+function renderFigure(){
+  const f = figures[idx];
+  document.getElementById("img").src = f.src;
+  document.getElementById("cap").innerHTML = `<strong>${f.title}</strong>`;
+  document.getElementById("interp").innerHTML =
+    `<strong>Interpretation</strong><br/>${f.interpretation}`;
+}
+
+function next(){ idx = (idx + 1) % figures.length; renderFigure(); }
+function prev(){ idx = (idx - 1 + figures.length) % figures.length; renderFigure(); }
+
+document.addEventListener("keydown", e => {
+  if (e.key === "ArrowRight") next();
+  if (e.key === "ArrowLeft") prev();
+});
+
+renderFigure();
+</script>
+
+</body>
+</html>
+
diff --git a/Strange/plot_dual_results.py b/Strange/plot_dual_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..821b750cee262b43d5f3daa2911f9626275f1e4e
--- /dev/null
+++ b/Strange/plot_dual_results.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+import sys
+from pathlib import Path
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+ROOT = Path(__file__).resolve().parent
+STRANGE_CSV = ROOT / "strange" / "results" / "strange_hypothesis_lab.csv"
+ANTISTRANGE_CSV = ROOT / "AntiStrange" / "results" / "antistrange_hypothesis_lab.csv"
+OUT_DIR = ROOT / "graphics"
+
+def _episode_return(df: pd.DataFrame) -> pd.DataFrame:
+    # Prefer explicit episode_return/return, else sum reward per episode.
+    if {"episode", "episode_return"}.issubset(df.columns):
+        s = df.groupby("episode")["episode_return"].mean().reset_index()
+        s = s.rename(columns={"episode_return": "value"})
+        return s
+    if {"episode", "return"}.issubset(df.columns):
+        s = df.groupby("episode")["return"].mean().reset_index()
+        s = s.rename(columns={"return": "value"})
+        return s
+    if {"episode", "reward"}.issubset(df.columns):
+        s = df.groupby("episode")["reward"].sum().reset_index()
+        s = s.rename(columns={"reward": "value"})
+        return s
+    raise KeyError(f"Need columns episode + (episode_return|return|reward). Found: {list(df.columns)}")
+
+def _episode_mean(df: pd.DataFrame, col: str) -> pd.DataFrame | None:
+    if {"episode", col}.issubset(df.columns):
+        s = df.groupby("episode")[col].mean().reset_index()
+        s = s.rename(columns={col: "value"})
+        return s
+    return None
+
+def main() -> int:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    if not STRANGE_CSV.exists():
+        print(f"[dual-plot] Missing {STRANGE_CSV}")
+        return 2
+    if not ANTISTRANGE_CSV.exists():
+        print(f"[dual-plot] Missing {ANTISTRANGE_CSV}")
+        return 2
+
+    df_s = pd.read_csv(STRANGE_CSV)
+    df_a = pd.read_csv(ANTISTRANGE_CSV)
+
+    # --- Dual episode return ---
+    s_ret = _episode_return(df_s)
+    a_ret = _episode_return(df_a)
+
+    plt.figure()
+    plt.plot(s_ret["episode"], s_ret["value"], label="Strange")
+    plt.plot(a_ret["episode"], a_ret["value"], label="AntiStrange")
+    plt.xlabel("episode")
+    plt.ylabel("episode_return")
+    plt.title("Strange vs AntiStrange — Episode Return")
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(OUT_DIR / "dual_episode_return.png", dpi=180)
+    plt.close()
+
+    # --- Optional dual stability ---
+    s_stab = _episode_mean(df_s, "stability")
+    a_stab = _episode_mean(df_a, "stability")
+    if s_stab is not None or a_stab is not None:
+        plt.figure()
+        if s_stab is not None:
+            plt.plot(s_stab["episode"], s_stab["value"], label="Strange")
+        if a_stab is not None:
+            plt.plot(a_stab["episode"], a_stab["value"], label="AntiStrange")
+        plt.xlabel("episode")
+        plt.ylabel("stability (mean per episode)")
+        plt.title("Strange vs AntiStrange — Stability")
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(OUT_DIR / "dual_stability.png", dpi=180)
+        plt.close()
+
+    print(f"[dual-plot] Saved plots to {OUT_DIR}")
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/Strange/run_all.bat b/Strange/run_all.bat
new file mode 100644
index 0000000000000000000000000000000000000000..3c588926de39db9a05fd5bc6a3296283615221bb
--- /dev/null
+++ b/Strange/run_all.bat
@@ -0,0 +1,3 @@
+@echo off
+REM TwoQuarks Strange runner (Strange + AntiStrange)
+python run_all.py
diff --git a/Strange/run_all.py b/Strange/run_all.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ed80339333a568c2b0247d51634b893f637c90a
--- /dev/null
+++ b/Strange/run_all.py
@@ -0,0 +1,43 @@
+import os
+import sys
+import subprocess
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+STRANGE_DIR = ROOT / "strange"
+ANTISTRANGE_DIR = ROOT / "AntiStrange"
+OUT_GRAPHICS = ROOT / "graphics"
+
+def _run(cmd, cwd: Path):
+    print(f"[run] ({cwd}) $ {' '.join(cmd)}")
+    subprocess.run(cmd, cwd=str(cwd), check=True)
+
+def main():
+    py = sys.executable
+    OUT_GRAPHICS.mkdir(parents=True, exist_ok=True)
+
+    # 1) Run Strange
+    _run([py, str(STRANGE_DIR / "exp" / "run_strange_hypothesis_lab.py")], cwd=STRANGE_DIR)
+
+    # 2) Plot Strange -> ROOT/graphics
+    _run([py, str(STRANGE_DIR / "exp" / "plot_results.py"),
+          "--csv", str(STRANGE_DIR / "results" / "strange_hypothesis_lab.csv"),
+          "--out", str(OUT_GRAPHICS),
+          "--prefix", "Strange"], cwd=STRANGE_DIR)
+
+    # 3) Run AntiStrange
+    _run([py, str(ANTISTRANGE_DIR / "exp" / "run_antistrange_hypothesis_lab.py")], cwd=ANTISTRANGE_DIR)
+
+    # 4) Plot AntiStrange -> ROOT/graphics
+    _run([py, str(ANTISTRANGE_DIR / "exp" / "plot_results.py"),
+          "--csv", str(ANTISTRANGE_DIR / "results" / "antistrange_hypothesis_lab.csv"),
+          "--out", str(OUT_GRAPHICS),
+          "--prefix", "AntiStrange"], cwd=ANTISTRANGE_DIR)
+
+    # 5) Dual plot (both on same figure)
+    _run([py, str(ROOT / "plot_dual_results.py")], cwd=ROOT)
+
+    print(f"[ok] Finished. All plots saved to: {OUT_GRAPHICS}")
+
+if __name__ == "__main__":
+    main()
diff --git a/Strange/run_all.sh b/Strange/run_all.sh
new file mode 100644
index 0000000000000000000000000000000000000000..71da0a462f563462690830fcf08c9c5f1260d29b
--- /dev/null
+++ b/Strange/run_all.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+python3 run_all.py
diff --git a/Strange/strange/envs/__init__.py b/Strange/strange/envs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/strange/envs/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/strange/envs/dual_hypothesis_lab_env.py b/Strange/strange/envs/dual_hypothesis_lab_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3515f447ba260c2793779e248fa1f7aeece3fe
--- /dev/null
+++ b/Strange/strange/envs/dual_hypothesis_lab_env.py
@@ -0,0 +1,265 @@
+# ======================================================================
+# DualHypothesisLabEnv
+# ----------------------------------------------------------------------
+# Entorno donde STRANGE y ANTI-STRANGE convergen sobre el MISMO estado.
+#
+# - phase:   0 = meseta, 1 = ambigua, 2 = claridad profunda
+# - progress:  superficie (0..1)
+# - stability: superficie (0..1)
+# - latent_potential: nivel "subterráneo" (0..1) que anti-STRANGE excava
+#
+# STRANGE actúa sobre progress/stability (como HypothesisLabEnv).
+# ANTI-STRANGE actúa sobre latent_potential y mete ruido estructurado
+# que puede desviar el curso cuando hay mucha energía latente.
+#
+# Ambos reciben el MISMO reward global.
+# ======================================================================
+
+from dataclasses import dataclass
+import numpy as np
+
+
+@dataclass
+class DualHypothesisState:
+    phase: int
+    progress: float
+    stability: float
+    latent_potential: float
+    last_action_strange: int
+    last_action_anti: int
+
+
+class DualHypothesisLabEnv:
+    """
+    Entorno de convergencia STRANGE + ANTI-STRANGE.
+
+    step(a_strange, a_anti) -> (next_state_id, reward, done, info)
+
+    - Usa las tablas de HypothesisLabEnv para el efecto "superficie"
+      (progress, stability) controlado por STRANGE.
+    - Usa las tablas de AntiHypothesisLabEnv para el efecto "subsuelo"
+      (latent_potential) controlado por ANTI-STRANGE.
+    - La fase puede saltar a 2 cuando la latent_potential supera un
+      umbral, simulando "descubrimiento profundo".
+    """
+
+    def __init__(
+        self,
+        max_steps: int = 50,
+        phase_drift_prob: float = 0.04,
+        potential_threshold: float = 0.6,
+        seed: int = 0,
+    ):
+        self.max_steps = max_steps
+        self.phase_drift_prob = phase_drift_prob
+        self.potential_threshold = potential_threshold
+        self.rng = np.random.default_rng(seed)
+        self.step_count = 0
+        self.state: DualHypothesisState | None = None
+
+    # Ambos hadrones comparten el mismo espacio de acción (0..3)
+    @property
+    def n_actions(self) -> int:
+        return 4
+
+    # ----------------- Utils de estado -----------------
+
+    def _encode_state(self, s: DualHypothesisState) -> int:
+        """
+        Codificación discreta:
+        3 fases x 5 bins de progreso x 5 de estabilidad x 3 de potencial
+        = 225 estados.
+        """
+        p_bin = int(np.clip(s.progress * 5, 0, 4))
+        st_bin = int(np.clip(s.stability * 5, 0, 4))
+        pot_bin = int(np.clip(s.latent_potential * 3, 0, 2))
+        return s.phase * 75 + p_bin * 15 + st_bin * 3 + pot_bin
+
+    def _sample_next_phase(self, phase: int, latent_potential: float) -> int:
+        """
+        Regla de fase híbrida:
+
+        - Si la energía latente cruza el umbral, saltamos a fase 2.
+        - Si no, hacemos drift local como en HypothesisLabEnv.
+        """
+        if latent_potential >= self.potential_threshold and phase < 2:
+            return 2
+
+        if self.rng.random() > self.phase_drift_prob:
+            return phase
+
+        delta = self.rng.integers(-1, 2)
+        next_phase = int(np.clip(phase + delta, 0, 2))
+        return next_phase
+
+    # ----------------- Reset -----------------
+
+    def reset(self) -> int:
+        self.step_count = 0
+        phase = int(self.rng.integers(0, 2))  # meseta / ambigua
+        progress = 0.1
+        stability = 0.2
+        latent_potential = 0.0
+        self.state = DualHypothesisState(
+            phase=phase,
+            progress=progress,
+            stability=stability,
+            latent_potential=latent_potential,
+            last_action_strange=-1,
+            last_action_anti=-1,
+        )
+        return self._encode_state(self.state)
+
+    # ----------------- Dinámica núcleo -----------------
+
+    def _surface_deltas(self, phase: int, a_strange: int):
+        """
+        Dinámica "superficie" copiada de HypothesisLabEnv,
+        gobernada por la acción de STRANGE.
+        """
+        # d_progress, d_stability, base_reward
+        if phase == 0:   # meseta
+            if a_strange == 0:
+                return 0.10, -0.02, 0.05
+            elif a_strange == 1:
+                return 0.03, 0.02, 0.02
+            elif a_strange == 2:
+                return 0.07, 0.00, 0.04
+            else:
+                return 0.01, 0.01, 0.00
+        elif phase == 1:  # ambigua
+            if a_strange == 0:
+                return 0.04, -0.03, 0.01
+            elif a_strange == 1:
+                return 0.05, 0.06, 0.05
+            elif a_strange == 2:
+                return 0.03, 0.03, 0.04
+            else:
+                return 0.02, 0.01, 0.02
+        else:  # claridad
+            if a_strange == 0:
+                return 0.03, -0.04, 0.00
+            elif a_strange == 1:
+                return 0.04, 0.04, 0.05
+            elif a_strange == 2:
+                return 0.03, 0.02, 0.03
+            else:
+                return 0.06, 0.03, 0.08
+
+    def _latent_deltas(self, phase: int, a_anti: int):
+        """
+        Dinámica "subsuelo" basada en AntiHypothesisLabEnv.
+        Devuelve d_progress_extra, d_stability_extra, d_potential, base_r_anti
+        (los dos primeros van con peso bajo).
+        """
+        if phase == 0:
+            if a_anti == 0:
+                return 0.10, -0.03, 0.08, 0.02
+            elif a_anti == 1:
+                return 0.03, 0.02, 0.01, 0.03
+            elif a_anti == 2:
+                return 0.08, 0.00, 0.06, 0.03
+            else:
+                return 0.02, 0.02, 0.00, 0.02
+        elif phase == 1:
+            if a_anti == 0:
+                return 0.03, -0.04, 0.06, 0.00
+            elif a_anti == 1:
+                return 0.05, 0.06, 0.01, 0.06
+            elif a_anti == 2:
+                return 0.04, 0.03, 0.05, 0.05
+            else:
+                return 0.06, 0.06, 0.01, 0.08
+        else:
+            if a_anti == 0:
+                return 0.02, -0.05, -0.02, 0.00
+            elif a_anti == 1:
+                return 0.05, 0.05, -0.01, 0.07
+            elif a_anti == 2:
+                return 0.05, 0.03, -0.005, 0.06
+            else:
+                return 0.08, 0.05, -0.03, 0.12
+
+    # ----------------- Step conjunto -----------------
+
+    def step(self, a_strange: int, a_anti: int):
+        """
+        Paso conjunto:
+
+        - STRANGE elige a_strange.
+        - ANTI-STRANGE elige a_anti.
+        - El entorno combina ambas influencias y devuelve:
+            next_state_id, reward_global, done, info
+        """
+        assert self.state is not None, "Llama a reset() antes de step()"
+        self.step_count += 1
+        s = self.state
+        phase = s.phase
+
+        # Dinámica de superficie (STRANGE)
+        dP_s, dS_s, base_r_s = self._surface_deltas(phase, a_strange)
+
+        # Dinámica de subsuelo (ANTI-STRANGE)
+        dP_a, dS_a, dPot, base_r_a = self._latent_deltas(phase, a_anti)
+
+        # Mezcla: STRANGE domina la superficie, ANTI añade un 30% de ruido estructurado
+        d_progress = dP_s + 0.3 * dP_a
+        d_stability = dS_s + 0.3 * dS_a
+
+        # Ruido leve
+        d_progress += self.rng.normal(0.0, 0.01)
+        d_stability += self.rng.normal(0.0, 0.01)
+
+        new_progress = float(np.clip(s.progress + d_progress, 0.0, 1.0))
+        new_stability = float(np.clip(s.stability + d_stability, 0.0, 1.0))
+        new_pot = float(np.clip(s.latent_potential + dPot, 0.0, 1.0))
+
+        # Reward global:
+        #   - Superficie (como HypothesisLabEnv)
+        #   - Potencial latente
+        #   - Sinergia cuando las acciones son distintas y hay potencial alto
+        reward_surface = base_r_s + 0.4 * new_progress + 0.3 * new_stability
+        reward_latent = 0.2 * new_pot + 0.3 * base_r_a
+
+        synergy = 0.0
+        if a_strange != a_anti and new_pot > 0.5:
+            synergy += 0.03
+        if a_strange == a_anti and new_pot < 0.2:
+            # si ambos se quedan pegados a lo mismo con potencial bajo: castigo
+            synergy -= 0.02
+
+        reward = reward_surface + reward_latent + synergy
+
+        # Penalización suave por repetir exactamente la misma acción
+        if a_strange == s.last_action_strange:
+            reward -= 0.01
+        if a_anti == s.last_action_anti:
+            reward -= 0.01
+
+        # Penalización si todo está hundido
+        if new_progress < 0.15 and new_stability < 0.15 and new_pot < 0.15:
+            reward -= 0.08
+
+        # Actualizar fase
+        next_phase = self._sample_next_phase(phase, new_pot)
+
+        # Actualizar estado
+        self.state = DualHypothesisState(
+            phase=next_phase,
+            progress=new_progress,
+            stability=new_stability,
+            latent_potential=new_pot,
+            last_action_strange=a_strange,
+            last_action_anti=a_anti,
+        )
+
+        done = self.step_count >= self.max_steps
+
+        info = {
+            "phase": next_phase,
+            "progress": new_progress,
+            "stability": new_stability,
+            "latent_potential": new_pot,
+        }
+
+        return self._encode_state(self.state), float(reward), bool(done), info
diff --git a/Strange/strange/envs/hypothesis_lab_env.py b/Strange/strange/envs/hypothesis_lab_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9204f90b4450a74303a64a45afb8815e77b495f
--- /dev/null
+++ b/Strange/strange/envs/hypothesis_lab_env.py
@@ -0,0 +1,156 @@
+
+"""
+HypothesisLabEnv
+-----------------
+Entorno abstracto tipo "laboratorio de hipótesis".
+
+- El agente decide entre acciones de exploración, refinamiento y consolidación.
+- El entorno tiene 3 fases (regímenes) que cambian con el tiempo:
+    0: Meseta (stuck)        -> explorar ayuda más.
+    1: Zona ambigua (ruido)  -> combinar / refinar ayuda.
+    2: Claridad (explotar)   -> consolidar la hipótesis principal.
+
+Estado (discreto):
+    (phase, progress_bin, stability_bin)
+que se empaqueta en un entero para que sea fácil usar tabular Q.
+"""
+
+import numpy as np
+from dataclasses import dataclass
+
+ACTIONS = {
+    0: "explore_new",      # probar experimento radical nuevo
+    1: "refine_current",   # mejorar hipótesis actual
+    2: "combine_paths",    # combinar líneas previas
+    3: "exploit_main"      # consolidar y explotar la mejor
+}
+N_ACTIONS = len(ACTIONS)
+
+
+@dataclass
+class HypothesisState:
+    phase: int          # 0,1,2
+    progress: float     # [0,1]
+    stability: float    # [0,1] (qué tan claros son los resultados)
+
+
+class HypothesisLabEnv:
+    """
+    Entorno minimalista pero no trivial.
+
+    Reward intuitivo:
+    - Fase 0 (meseta): explorar/combine mejoran más el progreso.
+    - Fase 1 (ambigua): refine/combine ayudan a subir estabilidad.
+    - Fase 2 (claridad): exploit/refine consolidan y dan mayor reward.
+
+    Además, el entorno cambia de fase con una dinámica lenta, para
+    forzar al swarm STRANGE a detectar el régimen mediante métricas
+    internas, no observables explícitos.
+    """
+    def __init__(self,
+                 max_steps: int = 50,
+                 phase_drift_prob: float = 0.05,
+                 seed: int = 0):
+        self.max_steps = max_steps
+        self.rng = np.random.default_rng(seed)
+        self.phase_drift_prob = phase_drift_prob
+        self.step_count = 0
+        self.state = None
+
+    @property
+    def n_actions(self):
+        return N_ACTIONS
+
+    def _sample_next_phase(self, phase: int) -> int:
+        """Cambia de fase con prob pequeña y movimiento local."""
+        if self.rng.random() > self.phase_drift_prob:
+            return phase
+        # moverse -1,0,+1 pero quedando en 0..2
+        delta = self.rng.integers(-1, 2)
+        next_phase = int(np.clip(phase + delta, 0, 2))
+        return next_phase
+
+    def reset(self):
+        self.step_count = 0
+        # empezamos casi siempre en meseta-ambigua
+        phase = int(self.rng.integers(0, 2))
+        progress = 0.1
+        stability = 0.2
+        self.state = HypothesisState(phase, progress, stability)
+        return self._encode_state(self.state)
+
+    def _encode_state(self, s: HypothesisState) -> int:
+        # 3 fases, 5 bins de progreso, 5 de estabilidad -> 75 estados
+        p_bin = int(np.clip(s.progress * 5, 0, 4))
+        st_bin = int(np.clip(s.stability * 5, 0, 4))
+        return s.phase * 25 + p_bin * 5 + st_bin
+
+    def step(self, action: int):
+        """
+        Devuelve: next_state_id, reward, done, info.
+        """
+        assert 0 <= action < N_ACTIONS
+        self.step_count += 1
+        s = self.state
+
+        # Efecto base de la acción sobre progreso y estabilidad
+        d_progress = 0.0
+        d_stability = 0.0
+
+        # Matriz efecto (phase, action) -> (d_prog, d_stab, base_reward)
+        # Valores hechos a mano para inducir multi-modalidad.
+        phase = s.phase
+        if phase == 0:   # meseta
+            if action == 0:  # explore_new
+                d_progress, d_stability, base_r = 0.10, -0.02, 0.05
+            elif action == 1:  # refine
+                d_progress, d_stability, base_r = 0.03, 0.02, 0.02
+            elif action == 2:  # combine
+                d_progress, d_stability, base_r = 0.07, 0.00, 0.04
+            else:  # exploit
+                d_progress, d_stability, base_r = 0.01, 0.01, 0.0
+        elif phase == 1:  # ambigua
+            if action == 0:
+                d_progress, d_stability, base_r = 0.04, -0.03, 0.01
+            elif action == 1:
+                d_progress, d_stability, base_r = 0.05, 0.06, 0.05
+            elif action == 2:
+                d_progress, d_stability, base_r = 0.03, 0.03, 0.04
+            else:  # exploit
+                d_progress, d_stability, base_r = 0.02, 0.01, 0.02
+        else:  # phase == 2, claridad
+            if action == 0:
+                d_progress, d_stability, base_r = 0.03, -0.04, 0.0
+            elif action == 1:
+                d_progress, d_stability, base_r = 0.04, 0.04, 0.05
+            elif action == 2:
+                d_progress, d_stability, base_r = 0.03, 0.02, 0.03
+            else:  # exploit
+                d_progress, d_stability, base_r = 0.06, 0.03, 0.08
+
+        # Ruido leve
+        d_progress += self.rng.normal(0.0, 0.01)
+        d_stability += self.rng.normal(0.0, 0.01)
+
+        new_progress = float(np.clip(s.progress + d_progress, 0.0, 1.0))
+        new_stability = float(np.clip(s.stability + d_stability, 0.0, 1.0))
+
+        # Reward combina:
+        # - progreso
+        # - estabilidad alta
+        # - penalización si todo muy bajo
+        reward = base_r
+        reward += 0.4 * new_progress + 0.3 * new_stability
+        if new_progress < 0.2 and new_stability < 0.2:
+            reward -= 0.1
+
+        next_phase = self._sample_next_phase(phase)
+        self.state = HypothesisState(next_phase, new_progress, new_stability)
+        done = self.step_count >= self.max_steps
+
+        info = {
+            "phase": next_phase,
+            "progress": new_progress,
+            "stability": new_stability
+        }
+        return self._encode_state(self.state), float(reward), bool(done), info
diff --git a/Strange/strange/exp/__init__.py b/Strange/strange/exp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/strange/exp/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/strange/exp/plot_results.py b/Strange/strange/exp/plot_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..eed8b6fb93909b6760dd47ee6d24ccc19ba7dd0c
--- /dev/null
+++ b/Strange/strange/exp/plot_results.py
@@ -0,0 +1,64 @@
+
+import os
+import sys
+from pathlib import Path
+
+THIS_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = THIS_DIR.parent
+sys.path.append(str(PROJECT_ROOT))
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def plot_from_csv(csv_path: Path, out_dir: Path, prefix: str) -> None:
+    df = pd.read_csv(csv_path)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    # Episode return
+    if {"episode","reward"}.issubset(df.columns):
+        ep_ret = df.groupby("episode")["reward"].sum().reset_index()
+        plt.figure()
+        plt.plot(ep_ret["episode"], ep_ret["reward"])
+        plt.xlabel("episode")
+        plt.ylabel("episode_return")
+        plt.title(f"{prefix} episode return")
+        plt.tight_layout()
+        plt.savefig(out_dir / f"{prefix.lower()}_episode_return.png", dpi=160)
+        plt.close()
+
+    # Stability / diversity if present
+    for col in ["stability","diversity","phase","progress","w_t","H_swarm","S_t","T"]:
+        if col in df.columns:
+            series = df.groupby("episode")[col].mean().reset_index()
+            plt.figure()
+            plt.plot(series["episode"], series[col])
+            plt.xlabel("episode")
+            plt.ylabel(col)
+            plt.title(f"{prefix} mean {col}")
+            plt.tight_layout()
+            plt.savefig(out_dir / f"{prefix.lower()}_{col}.png", dpi=160)
+            plt.close()
+
+def main():
+    import argparse
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--csv", required=True, help="Path to results CSV (relative to project root is ok).")
+    ap.add_argument("--out", default="graphics", help="Output directory for plots (relative ok).")
+    ap.add_argument("--prefix", default="Strange", help="Plot filename prefix.")
+    args = ap.parse_args()
+
+    csv_path = Path(args.csv)
+    if not csv_path.is_absolute():
+        csv_path = (PROJECT_ROOT / csv_path).resolve()
+    out_dir = Path(args.out)
+    if not out_dir.is_absolute():
+        out_dir = (PROJECT_ROOT / out_dir).resolve()
+
+    if not csv_path.exists():
+        raise FileNotFoundError(f"CSV not found: {csv_path}")
+
+    plot_from_csv(csv_path, out_dir, args.prefix)
+    print(f"[ok] plots saved to: {out_dir}")
+
+if __name__ == "__main__":
+    main()
diff --git a/Strange/strange/exp/run_strange_hypothesis_lab.py b/Strange/strange/exp/run_strange_hypothesis_lab.py
new file mode 100644
index 0000000000000000000000000000000000000000..a786dc4e72319c596da4c601c471122e4fba745e
--- /dev/null
+++ b/Strange/strange/exp/run_strange_hypothesis_lab.py
@@ -0,0 +1,70 @@
+
+
+import os, sys
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.abspath(os.path.join(THIS_DIR, ".."))
+sys.path.append(PROJECT_ROOT)
+
+def _resolve_path(p: str) -> str:
+    """Resolve relative paths against PROJECT_ROOT for reproducible runs."""
+    if os.path.isabs(p):
+        return p
+    return os.path.abspath(os.path.join(PROJECT_ROOT, p))
+
+import os
+import csv
+import numpy as np
+from envs.hypothesis_lab_env import HypothesisLabEnv
+from levo.strange_swarm import StrangeConfig, StrangeSwarm
+
+
+def run_experiment(
+    n_episodes: int = 200,
+    max_steps: int = 50,
+    seed: int = 0,
+    out_path: str = "results/strange_hypothesis_lab.csv",
+):
+    env = HypothesisLabEnv(max_steps=max_steps, seed=seed)
+    out_path = _resolve_path(out_path)
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+
+    cfg = StrangeConfig(n_actions=env.n_actions, seed=seed)
+    swarm = StrangeSwarm(cfg)
+
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    fieldnames = [
+        "episode", "step", "reward", "T", "H_swarm", "S_t", "diversity",
+        "w_t", "phase", "progress", "stability"
+    ]
+    with open(out_path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+
+        for ep in range(n_episodes):
+            s = env.reset()
+            done = False
+            step = 0
+            while not done:
+                a, info = swarm.act(s, step)
+                s_next, r, done, env_info = env.step(a)
+                swarm.update(s, a, r, s_next, done)
+                row = {
+                    "episode": ep,
+                    "step": step,
+                    "reward": r,
+                    "T": info["T"],
+                    "H_swarm": info["H_swarm"],
+                    "S_t": info["S_t"],
+                    "diversity": info["diversity"],
+                    "w_t": info["w_t"],
+                    "phase": env_info["phase"],
+                    "progress": env_info["progress"],
+                    "stability": env_info["stability"],
+                }
+                writer.writerow(row)
+                s = s_next
+                step += 1
+
+
+if __name__ == "__main__":
+    run_experiment()
diff --git a/Strange/strange/graphics/index.html b/Strange/strange/graphics/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..d46d468f4873bea190afff54ce3f02e884e7cd4e
--- /dev/null
+++ b/Strange/strange/graphics/index.html
@@ -0,0 +1,147 @@
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+<title>CHARM • Graphics</title>
+<link rel="icon" href="/favicon.svg" type="image/svg+xml"/>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+:root{
+  --bg:#000; --fg:#fff; --muted:rgba(255,255,255,.62);
+  --card:rgba(255,255,255,.04); --stroke:rgba(255,255,255,.10); --glass:rgba(0,0,0,.38);
+}
+body{font-family:Inter,system-ui,Segoe UI,Roboto,Arial;background:var(--bg);color:var(--fg);overflow-x:hidden}
+#quantumField{position:fixed;inset:0;width:100vw;height:100vh;z-index:-10;background:#000}
+nav{position:fixed;top:0;left:0;right:0;z-index:1000;padding:14px 24px;background:var(--glass);backdrop-filter:blur(12px);border-bottom:1px solid rgba(255,255,255,.06)}
+.nav-inner{max-width:1200px;margin:0 auto;display:flex;align-items:center;gap:16px}
+.brand{font-weight:700;font-size:1.1rem}
+.nav-links{display:flex;gap:10px;margin-left:18px}
+.nav-links a{text-decoration:none;color:var(--fg);padding:8px 12px;border:1px solid rgba(255,255,255,.12);border-radius:999px;font-size:.9rem;opacity:.86;transition:.15s ease;white-space:nowrap}
+.nav-links a:hover{opacity:1;background:rgba(255,255,255,.06)}
+.spacer{flex:1}
+.menu-btn{width:40px;height:40px;display:flex;align-items:center;justify-content:center;border:1px solid rgba(255,255,255,.18);border-radius:12px;cursor:pointer;background:rgba(255,255,255,.02)}
+.menu-btn span{display:block;width:18px;height:2px;background:#fff;position:relative}
+.menu-btn span::before,.menu-btn span::after{content:"";position:absolute;left:0;width:18px;height:2px;background:#fff}
+.menu-btn span::before{top:-6px} .menu-btn span::after{top:6px}
+.menu{position:absolute;top:60px;right:24px;min-width:220px;padding:10px;border-radius:14px;background:var(--glass);backdrop-filter:blur(14px);border:1px solid rgba(255,255,255,.10);box-shadow:0 0 30px rgba(0,0,0,.6);display:none}
+.menu.open{display:block}
+.menu a{display:block;padding:10px 10px;border-radius:10px;text-decoration:none;color:var(--fg);opacity:.9}
+.menu a:hover{background:rgba(255,255,255,.06);opacity:1}
+.menu .sep{height:1px;background:rgba(255,255,255,.10);margin:8px 2px}
+.wrap{max-width:1100px;margin:0 auto;padding:110px 6vw 70px}
+.h{font-size:1.35rem;font-weight:650}
+.sub{color:var(--muted);margin-top:10px;line-height:1.6;max-width:980px}
+.viewer{margin-top:20px;display:grid;gap:14px;justify-items:center}
+img{width:100%;max-width:1000px;max-height:70vh;object-fit:contain;border-radius:14px;background:rgba(255,255,255,.03);border:1px solid rgba(255,255,255,.10)}
+.controls{display:flex;gap:10px;flex-wrap:wrap;justify-content:center}
+.btn{text-decoration:none;color:var(--fg);padding:10px 14px;border-radius:999px;border:1px solid rgba(255,255,255,.14);background:rgba(255,255,255,.03);opacity:.92;transition:.15s ease}
+.btn:hover{opacity:1;background:rgba(255,255,255,.07)}
+.kbd{font-family:ui-monospace,Menlo,Consolas,monospace;background:#15151b;border:1px solid #2a2a33;border-radius:6px;padding:2px 6px;font-size:.85em;color:var(--muted)}
+.cap{color:var(--muted);max-width:980px;line-height:1.6;text-align:center}
+.rule{border:0;border-top:1px solid rgba(255,255,255,.10);margin:22px 0}
+</style>
+</head>
+<body>
+<canvas id="quantumField"></canvas>
+
+<nav>
+  <div class="nav-inner">
+    <div class="brand">TwoQuarks</div>
+    <div class="nav-links">
+      <a href="../../../index.html#down">DOWN</a>
+      <a href="../../../index.html#strange">STRANGE</a>
+      <a href="../../../index.html#top">TOP</a>
+      <a href="../../../index.html#charm">CHARM</a>
+      <a href="../../../index.html#up">UP</a>
+      <a href="../../../index.html#bottom">BOTTOM</a>
+    </div>
+    <div class="spacer"></div>
+    <div class="menu-btn" id="menuBtn"><span></span></div>
+    <div class="menu" id="siteMenu">
+      <a href="../../../index.html#about">ABOUT</a>
+      <a href="../../../quarkslab.html">QuarksLab</a>
+      <div class="sep"></div>
+      <a href="../../../quarks/bottom/resume.pdf">Resume</a>
+      <a href="../../../summary.pdf">Summary</a>
+    </div>
+  </div>
+</nav>
+
+<div class="wrap">
+  <div class="h">CHARM · Visual Diagnostics</div>
+  <div class="sub">These figures show robustness to heavy noise, keeping exploration controlled and policies coherent.</div>
+
+  <div class="viewer">
+    <img id="img" src="../anticharm/graphics/figure_01.png" alt="figure"/>
+    <div class="controls">
+      <a class="btn" href="javascript:void(0)" onclick="prev()">← Previous</a>
+      <a class="btn" href="javascript:void(0)" onclick="next()">Next →</a>
+      <span class="kbd">←</span><span class="kbd">→</span>
+    </div>
+    <div class="cap" id="cap">Figure 01</div>
+  </div>
+
+  <hr class="rule"/>
+  <div class="sub"><strong>Interpretation</strong><br/>These figures show robustness to heavy noise, keeping exploration controlled and policies coherent.</div>
+</div>
+
+<script>
+
+const canvas = document.getElementById('quantumField');
+const ctx = canvas.getContext('2d');
+function resize(){
+  canvas.width = innerWidth;
+  canvas.height = innerHeight;
+}
+resize();
+addEventListener('resize', resize);
+
+const stars = Array.from({length: 650}, () => ({
+  x: (Math.random()-0.5) * canvas.width,
+  y: (Math.random()-0.5) * canvas.height,
+  z: Math.random() * canvas.width
+}));
+
+function animate(){
+  ctx.clearRect(0,0,canvas.width,canvas.height);
+  for(const s of stars){
+    s.z -= 2.2;
+    if (s.z < 1){
+      s.x = (Math.random()-0.7) * canvas.width;
+      s.y = (Math.random()-0.7) * canvas.height;
+      s.z = canvas.width;
+    }
+    const k = 128 / s.z;
+    const x = s.x * k + canvas.width/2;
+    const y = s.y * k + canvas.height/2;
+    const r = (1 - s.z/canvas.width) * 1.2;
+    ctx.beginPath();
+    ctx.fillStyle = "rgba(140,180,255,0.85)";
+    ctx.arc(x,y,r,0,Math.PI*2);
+    ctx.fill();
+  }
+  requestAnimationFrame(animate);
+}
+animate();
+
+</script>
+<script>
+const menuBtn = document.getElementById('menuBtn');
+const siteMenu = document.getElementById('siteMenu');
+menuBtn.addEventListener('click', (e)=>{
+  e.stopPropagation(); siteMenu.classList.toggle('open');
+});
+document.addEventListener('click', ()=> siteMenu.classList.remove('open'));
+siteMenu.addEventListener('click', (e)=> e.stopPropagation());
+
+const images = [{"src": "anticharm/graphics/figure_01.png", "text": "Figure 01"}, {"src": "anticharm/graphics/figure_02.png", "text": "Figure 02"}, {"src": "anticharm/graphics/figure_03.png", "text": "Figure 03"}, {"src": "charm/graphics/figure_01.png", "text": "Figure 04"}, {"src": "charm/graphics/figure_02.png", "text": "Figure 05"}, {"src": "charm/graphics/figure_03.png", "text": "Figure 06"}, {"src": "freq_policies/graphics/figure_01.png", "text": "Figure 07"}, {"src": "freq_policies/graphics/figure_02.png", "text": "Figure 08"}, {"src": "freq_policies/graphics/figure_03.png", "text": "Figure 09"}, {"src": "z_olo/graphics/figure_01.png", "text": "Figure 10"}, {"src": "z_olo/graphics/figure_02.png", "text": "Figure 11"}, {"src": "z_olo/graphics/figure_03.png", "text": "Figure 12"}];
+let i = 0;
+function show(){ document.getElementById("img").src = images[i].src; document.getElementById("cap").textContent = images[i].text; }
+function next(){ i=(i+1)%images.length; show(); }
+function prev(){ i=(i-1+images.length)%images.length; show(); }
+document.addEventListener("keydown",(e)=>{ if(e.key==="ArrowRight") next(); if(e.key==="ArrowLeft") prev(); });
+</script>
+</body>
+</html>
diff --git a/Strange/strange/graphics/strange_H_swarm.png b/Strange/strange/graphics/strange_H_swarm.png
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new file mode 100644
index 0000000000000000000000000000000000000000..457595de4756ccb61b25bed3fece10227e2e749e
--- /dev/null
+++ b/Strange/strange/graphics/strange_diversity.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5b9650da2a28bb92e4f9f5ee749601d66b27144f68ec4b6b418a4915e3b730
+size 105829
diff --git a/Strange/strange/graphics/strange_episode_return.png b/Strange/strange/graphics/strange_episode_return.png
new file mode 100644
index 0000000000000000000000000000000000000000..258f2fa9d931b32edf55ff50bfafd815288230af
--- /dev/null
+++ b/Strange/strange/graphics/strange_episode_return.png
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diff --git a/Strange/strange/graphics/strange_phase.png b/Strange/strange/graphics/strange_phase.png
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
+oid sha256:55943ee2a1a3f33382d22c166367386e156ac5f8c195a80807f7ceb146d2249e
+size 151199
diff --git a/Strange/strange/graphics/strange_progress.png b/Strange/strange/graphics/strange_progress.png
new file mode 100644
index 0000000000000000000000000000000000000000..ddb07f1ed13fea004699391b09e84c336d3f2e95
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+++ b/Strange/strange/graphics/strange_progress.png
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diff --git a/Strange/strange/graphics/strange_stability.png b/Strange/strange/graphics/strange_stability.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b3a2abc79e3296b5b2a83ee6b824c4abc6921b9aeefe1e3c8905d807e67b865
+size 140393
diff --git a/Strange/strange/graphics/strange_w_t.png b/Strange/strange/graphics/strange_w_t.png
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diff --git a/Strange/strange/levo/__init__.py b/Strange/strange/levo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03d47fc653525e099e016f51e288a14983a432e9
--- /dev/null
+++ b/Strange/strange/levo/__init__.py
@@ -0,0 +1 @@
+# Package init
diff --git a/Strange/strange/levo/antistrange_swarm.py b/Strange/strange/levo/antistrange_swarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff65a2553aee9960d04b4a8379408e426207ab0b
--- /dev/null
+++ b/Strange/strange/levo/antistrange_swarm.py
@@ -0,0 +1,359 @@
+"""
+ANTI-STRANGE Swarm
+------------------
+Contraparte "antiquark" del hadrón STRANGE.
+
+Diferencias conceptuales con StrangeSwarm:
+
+- Misma base HF-Levo tabular, pero:
+  * Termodinámica responde distinto a ΔR (premia mejoras fuertes).
+  * Gate w_t se activa cuando el sistema está demasiado ordenado.
+  * Usa una memoria *repulsora*: anti-proto, que empuja a acciones
+    históricamente poco visitadas (huecos del mapa de políticas).
+
+- Interfaz compatible con StrangeSwarm:
+    AntiStrangeConfig, AntiStrangeSwarm.act(), AntiStrangeSwarm.update().
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Hashable
+import numpy as np
+import math
+
+
+# ===================== HF-Levo agent (copiado para independencia) =====================
+
+@dataclass
+class HFLevoAgent:
+    n_actions: int
+    alpha: float = 0.1
+    gamma: float = 0.99
+    tau_base: float = 1.0
+
+    def __post_init__(self):
+        # Q: dict[state][action] -> value
+        self.Q: Dict[Hashable, np.ndarray] = {}
+        # Fase fija por acción para inducir diversidad real entre acciones
+        self.action_phases = np.linspace(
+            0.0, 2 * math.pi, self.n_actions, endpoint=False
+        )
+
+    def _ensure_state(self, s: Hashable):
+        if s not in self.Q:
+            self.Q[s] = np.zeros(self.n_actions, dtype=float)
+
+    def policy(
+        self,
+        s: Hashable,
+        t: int,
+        T: float,
+        hf_amp: float,
+        hf_omega: float,
+        phase_offset: float,
+        rng: np.random.Generator,
+    ) -> np.ndarray:
+        """
+        HF-Levo:
+            scores = Q + A(T) * cos(w(T)*t + phi_h,a)
+            p ~ softmax(scores / tau(T))
+        """
+        self._ensure_state(s)
+        q = self.Q[s]
+
+        # temperatura de softmax: tau sube con T (más exploración)
+        tau = self.tau_base * (1.0 + 0.5 * T)
+
+        # oscilación por acción: desfase del agente + desfase propio por acción
+        osc_vec = hf_amp * np.cos(
+            hf_omega * t + phase_offset + self.action_phases
+        )
+        scores = q + osc_vec
+
+        # softmax numéricamente estable
+        z = scores - scores.max()
+        exp_z = np.exp(z / max(1e-8, tau))
+        p = exp_z / exp_z.sum()
+        return p
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        self._ensure_state(s)
+        self._ensure_state(s_next)
+        q = self.Q[s]
+        q_next = self.Q[s_next]
+        target = r if done else (r + self.gamma * float(q_next.max()))
+        td = target - q[a]
+        q[a] += self.alpha * td
+
+
+# ===================== Config ANTI-STRANGE =====================
+
+@dataclass
+class AntiStrangeConfig:
+    n_agents: int = 5
+    n_actions: int = 4
+    hf_amp_base: float = 0.15
+    hf_omega_base: float = 0.35
+
+    T_init: float = 0.7
+    T_min: float = 0.0
+    T_max: float = 2.0
+    eta_T: float = 0.05  # pasos de T
+
+    # --- Excitación (sube T) ---
+    # Queremos subir T cuando:
+    # - Hay mejoras fuertes (|ΔR| grande positiva).
+    # - El sistema está demasiado ordenado (baja entropía y baja diversidad).
+    eps_R_pos: float = 0.03    # mejora significativa de recompensa
+    H_low: float = 0.7         # entropía baja
+    D_low: float = 0.08        # diversidad baja
+
+    # --- Regulación (baja T) ---
+    # Queremos bajar T cuando:
+    # - El sistema se vuelve caótico (H o diversidad altas).
+    # - Hay caídas fuertes de recompensa.
+    H_high: float = 1.25       # entropía muy alta (~max)
+    D_high: float = 0.20       # diversidad muy alta
+    eps_crash: float = 0.03    # caída fuerte de reward
+
+    # --- Gate memoria repulsora ---
+    # Gate alto cuando el sistema está demasiado "congelado":
+    # T baja, S_t baja, H_swarm baja.
+    T_mid: float = 0.9
+    S_mid: float = 0.06
+    H_mid: float = 0.8
+
+    kT: float = 1.5
+    kS: float = 2.0
+    kH: float = 1.0
+
+    proto_alpha: float = 0.1
+
+    seed: int = 1  # distinto de Strange por defecto
+
+
+# ===================== ANTI-STRANGE Swarm =====================
+
+class AntiStrangeSwarm:
+    """
+    Hadron ANTI-STRANGE.
+
+    Diferencias clave vs StrangeSwarm:
+      - T sube cuando hay mejoras fuertes y orden excesivo.
+      - Gate w_t empuja a memoria REPULSORA (anti-proto).
+      - Diseñado para explorar huecos de la política del enjambre.
+
+    Interfaz:
+        anti = AntiStrangeSwarm(cfg)
+        s = env.reset()
+        for t in ...:
+            a, info = anti.act(s, t)
+            s_next, r, done, env_info = env.step(a)
+            anti.update(s, a, r, s_next, done)
+    """
+
+    def __init__(self, config: AntiStrangeConfig):
+        self.cfg = config
+        self.n_actions = config.n_actions
+        self.agents: List[HFLevoAgent] = [
+            HFLevoAgent(n_actions=config.n_actions)
+            for _ in range(config.n_agents)
+        ]
+        self.rng = np.random.default_rng(config.seed)
+
+        # estado interno
+        self.T = config.T_init
+        self.proto: Dict[Hashable, np.ndarray] = {}
+
+        self.last_reward = 0.0
+        self.last_delta_R = 0.0
+
+        # fases diferentes para cada agente
+        self.agent_phases = np.linspace(
+            0.0, 2 * math.pi, config.n_agents, endpoint=False
+        )
+
+    # --------- Métricas internas ---------
+    @staticmethod
+    def _entropy(p: np.ndarray) -> float:
+        p_safe = np.clip(p, 1e-8, 1.0)
+        return float(-(p_safe * np.log(p_safe)).sum())
+
+    @staticmethod
+    def _diversity(ps: np.ndarray) -> float:
+        H, A = ps.shape
+        if H <= 1:
+            return 0.0
+        d = 0.0
+        cnt = 0
+        for i in range(H):
+            for j in range(i + 1, H):
+                d += float(np.abs(ps[i] - ps[j]).mean())
+                cnt += 1
+        return d / max(1, cnt)
+
+    # --------- Core swarm policy ---------
+    def _compute_swarm_policy(self, s: Hashable, t: int):
+        cfg = self.cfg
+        ps = []
+        for h, agent in enumerate(self.agents):
+            phase = self.agent_phases[h]
+            p_h = agent.policy(
+                s,
+                t,
+                self.T,
+                hf_amp=cfg.hf_amp_base * (1.0 + 0.5 * self.T),
+                hf_omega=cfg.hf_omega_base * (1.0 + 0.25 * self.T),
+                phase_offset=phase,
+                rng=self.rng,
+            )
+            ps.append(p_h)
+        ps = np.stack(ps)  # [H, A]
+        p_swarm = ps.mean(axis=0)
+        var = ps.var(axis=0)
+        S_t = float(var.mean())
+        H_swarm = self._entropy(p_swarm)
+        diversity = self._diversity(ps)
+        return p_swarm, S_t, H_swarm, diversity
+
+    def _update_proto(self, s: Hashable, p_swarm: np.ndarray):
+        cfg = self.cfg
+        if s not in self.proto:
+            self.proto[s] = p_swarm.copy()
+        else:
+            self.proto[s] = (
+                (1.0 - cfg.proto_alpha) * self.proto[s]
+                + cfg.proto_alpha * p_swarm
+            )
+
+    def _anti_proto(self, s: Hashable) -> np.ndarray:
+        """
+        Construye memoria repulsora:
+            anti_proto ∝ 1 - proto
+        Es decir, favorece acciones históricamente poco usadas.
+        """
+        proto_s = self.proto[s]
+        raw = 1.0 - proto_s
+        raw = np.clip(raw, 1e-8, None)
+        return raw / raw.sum()
+
+    def _thermo_step(
+        self,
+        delta_R: float,
+        H_swarm: float,
+        S_t: float,
+        diversity: float,
+    ):
+        """
+        Control térmico "anti":
+
+        - E_t (excitación, sube T):
+            * |ΔR| grande positiva (mejora fuerte de recompensa).
+            * Orden excesivo: H_swarm baja y diversidad baja.
+
+        - R_t (regulación, baja T):
+            * Caos fuerte: H_swarm muy alta o diversidad muy alta.
+            * Crash de recompensa: ΔR muy negativa.
+        """
+        cfg = self.cfg
+
+        improving = delta_R > cfg.eps_R_pos
+        too_ordered = (H_swarm < cfg.H_low) and (diversity < cfg.D_low)
+
+        E_t = (
+            (1.0 if improving else 0.0) +
+            (1.0 if too_ordered else 0.0)
+        )
+
+        too_chaotic = (H_swarm > cfg.H_high) or (diversity > cfg.D_high)
+        crashing = delta_R < -cfg.eps_crash
+
+        R_t = (
+            (1.0 if too_chaotic else 0.0) +
+            (1.0 if crashing else 0.0)
+        )
+
+        self.T += cfg.eta_T * (E_t - R_t)
+        self.T = float(np.clip(self.T, cfg.T_min, cfg.T_max))
+        return float(E_t), float(R_t)
+
+    def _gate(self, H_swarm: float, S_t: float) -> float:
+        """
+        Gate invertido:
+
+        - Queremos w_t alto cuando el sistema está demasiado
+          ordenado y frío (baja entropía / baja varianza / T baja),
+          para empujar hacia acciones en los "huecos" (anti-proto).
+
+        - Cuando T, S_t y H_swarm son altos, w_t tiende a 0
+          y se deja actuar más a p_swarm.
+        """
+        cfg = self.cfg
+        x = (
+            cfg.kT * (cfg.T_mid - self.T) +
+            cfg.kS * (cfg.S_mid - S_t) +
+            cfg.kH * (cfg.H_mid - H_swarm)
+        )
+        w = 1.0 / (1.0 + math.exp(-x))
+        return float(w)
+
+    # --------- Interfaz pública ---------
+    def act(self, s: Hashable, t: int):
+        """
+        Calcula la política ANTI-STRANGE y samplea una acción.
+        Devuelve:
+            action, info_dict
+        """
+        p_swarm, S_t, H_swarm, diversity = self._compute_swarm_policy(s, t)
+        self._update_proto(s, p_swarm)
+
+        # feedback térmico con ΔR real del paso previo
+        delta_R = self.last_delta_R
+        E_t, R_t = self._thermo_step(delta_R, H_swarm, S_t, diversity)
+
+        # memoria repulsora
+        anti_proto_s = self._anti_proto(s)
+        w_t = self._gate(H_swarm, S_t)
+
+        p_final = (1.0 - w_t) * p_swarm + w_t * anti_proto_s
+        p_final = p_final / p_final.sum()
+
+        a = int(self.rng.choice(len(p_final), p=p_final))
+
+        info = {
+            "T": self.T,
+            "S_t": S_t,
+            "H_swarm": H_swarm,
+            "diversity": diversity,
+            "E_t": E_t,
+            "R_t": R_t,
+            "w_t": w_t,
+            "p_swarm": p_swarm,
+            "p_final": p_final,
+        }
+        return a, info
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        # Actualizamos Q de todos los agentes
+        for agent in self.agents:
+            agent.update(s, a, r, s_next, done)
+
+        # ΔR real
+        delta_R = r - self.last_reward
+        self.last_reward = r
+        self.last_delta_R = delta_R
+
+        return delta_R
diff --git a/Strange/strange/levo/strange_swarm.py b/Strange/strange/levo/strange_swarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b98485535e539d4194312315bc19d88322df41ea
--- /dev/null
+++ b/Strange/strange/levo/strange_swarm.py
@@ -0,0 +1,309 @@
+"""
+STRANGE Swarm
+-------------
+Implementa:
+
+- HF-Levo tabular para cada agente del swarm.
+- Control termodinámico T con dT/dt = E - R.
+- Proto-memoria por estado.
+- Gate w_t que mezcla p_swarm y proto, dando la política final.
+
+Este archivo no depende de un entorno específico; sólo asume:
+- estados discretos hashables (por ejemplo enteros),
+- acciones indexadas 0..n_actions-1.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Hashable
+import numpy as np
+import math
+
+
+# ===================== HF-Levo agent =====================
+
+@dataclass
+class HFLevoAgent:
+    n_actions: int
+    alpha: float = 0.1
+    gamma: float = 0.99
+    tau_base: float = 1.0
+
+    def __post_init__(self):
+        # Q: dict[state][action] -> value
+        self.Q: Dict[Hashable, np.ndarray] = {}
+        # Fase fija por acción para inducir diversidad real entre acciones
+        self.action_phases = np.linspace(
+            0.0, 2 * math.pi, self.n_actions, endpoint=False
+        )
+
+    def _ensure_state(self, s: Hashable):
+        if s not in self.Q:
+            self.Q[s] = np.zeros(self.n_actions, dtype=float)
+
+    def policy(
+        self,
+        s: Hashable,
+        t: int,
+        T: float,
+        hf_amp: float,
+        hf_omega: float,
+        phase_offset: float,
+        rng: np.random.Generator,
+    ) -> np.ndarray:
+        """
+        Devuelve distribución de prob de acciones usando HF-Levo:
+            scores = Q + A(T) * cos(w(T)*t + phi_h,a)
+            p ~ softmax(scores / tau(T))
+        """
+        self._ensure_state(s)
+        q = self.Q[s]
+
+        # temperatura de softmax: tau sube con T (más exploración)
+        tau = self.tau_base * (1.0 + 0.5 * T)
+
+        # oscilación por acción: desfase del agente + desfase propio por acción
+        osc_vec = hf_amp * np.cos(
+            hf_omega * t + phase_offset + self.action_phases
+        )
+        scores = q + osc_vec
+
+        # softmax numéricamente estable
+        z = scores - scores.max()
+        exp_z = np.exp(z / max(1e-8, tau))
+        p = exp_z / exp_z.sum()
+        return p
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        self._ensure_state(s)
+        self._ensure_state(s_next)
+        q = self.Q[s]
+        q_next = self.Q[s_next]
+        target = r if done else (r + self.gamma * float(q_next.max()))
+        td = target - q[a]
+        q[a] += self.alpha * td
+
+
+# ===================== Config STRANGE =====================
+
+@dataclass
+class StrangeConfig:
+    n_agents: int = 5
+    n_actions: int = 4
+    hf_amp_base: float = 0.15
+    hf_omega_base: float = 0.35
+
+    T_init: float = 0.7
+    T_min: float = 0.0
+    T_max: float = 2.0
+    eta_T: float = 0.05  # pasos de T más suaves
+
+    # umbrales E (excitación)
+    eps_R: float = 0.02      # estancamiento si |ΔR| pequeño
+    H_min: float = 0.7       # baja entropía = poca exploración
+    D_min: float = 0.10      # poca diversidad de políticas
+
+    # umbrales R (regulación)
+    S_max: float = 0.15      # strangeness muy alta
+    H_max: float = 1.25      # entropía casi máxima (~log(4)=1.386)
+    eps_crash: float = 0.02  # caída fuerte de reward
+
+    # gates (controlan cuánto pesa la memoria)
+    T_mid: float = 1.1
+    S_mid: float = 0.08
+    H_mid: float = 1.0
+
+    kT: float = 1.5
+    kS: float = 2.0
+    kH: float = 1.0
+
+    proto_alpha: float = 0.1
+
+    seed: int = 0
+
+
+# ===================== STRANGE Swarm =====================
+
+class StrangeSwarm:
+    """
+    Implementación de alto nivel del hadrón STRANGE.
+
+    Uso:
+        swarm = StrangeSwarm(config)
+        s = env.reset()
+        for t in ...:
+            action, info = swarm.act(s, t)
+            s_next, r, done, env_info = env.step(action)
+            swarm.update(s, action, r, s_next, done)
+    """
+    def __init__(self, config: StrangeConfig):
+        self.cfg = config
+        self.n_actions = config.n_actions
+        self.agents: List[HFLevoAgent] = [
+            HFLevoAgent(n_actions=config.n_actions)
+            for _ in range(config.n_agents)
+        ]
+        self.rng = np.random.default_rng(config.seed)
+
+        # fases internas
+        self.T = config.T_init
+        self.proto: Dict[Hashable, np.ndarray] = {}
+
+        # para métricas
+        self.last_reward = 0.0
+        self.last_delta_R = 0.0  # ΔR del paso previo
+
+        # fases diferentes para cada agente
+        self.agent_phases = np.linspace(
+            0.0, 2 * math.pi, config.n_agents, endpoint=False
+        )
+
+    # --------- Métricas internas ---------
+    @staticmethod
+    def _entropy(p: np.ndarray) -> float:
+        p_safe = np.clip(p, 1e-8, 1.0)
+        return float(-(p_safe * np.log(p_safe)).sum())
+
+    @staticmethod
+    def _diversity(ps: np.ndarray) -> float:
+        # media de distancias L1 entre políticas
+        H, A = ps.shape
+        if H <= 1:
+            return 0.0
+        d = 0.0
+        cnt = 0
+        for i in range(H):
+            for j in range(i + 1, H):
+                d += float(np.abs(ps[i] - ps[j]).mean())
+                cnt += 1
+        return d / max(1, cnt)
+
+    # --------- Core ---------
+    def _compute_swarm_policy(self, s: Hashable, t: int):
+        cfg = self.cfg
+        ps = []
+        for h, agent in enumerate(self.agents):
+            phase = self.agent_phases[h]
+            p_h = agent.policy(
+                s,
+                t,
+                self.T,
+                hf_amp=cfg.hf_amp_base * (1.0 + 0.5 * self.T),
+                hf_omega=cfg.hf_omega_base * (1.0 + 0.25 * self.T),
+                phase_offset=phase,
+                rng=self.rng,
+            )
+            ps.append(p_h)
+        ps = np.stack(ps)  # [H, A]
+        p_swarm = ps.mean(axis=0)
+        var = ps.var(axis=0)
+        S_t = float(var.mean())
+        H_swarm = self._entropy(p_swarm)
+        diversity = self._diversity(ps)
+        return p_swarm, S_t, H_swarm, diversity
+
+    def _update_proto(self, s: Hashable, p_swarm: np.ndarray):
+        cfg = self.cfg
+        if s not in self.proto:
+            self.proto[s] = p_swarm.copy()
+        else:
+            self.proto[s] = (
+                (1.0 - cfg.proto_alpha) * self.proto[s]
+                + cfg.proto_alpha * p_swarm
+            )
+
+    def _thermo_step(
+        self,
+        delta_R: float,
+        H_swarm: float,
+        S_t: float,
+        diversity: float,
+    ):
+        cfg = self.cfg
+        # Excitación
+        E_t = (
+            (abs(delta_R) < cfg.eps_R) * 1.0 +
+            (H_swarm < cfg.H_min) * 1.0 +
+            (diversity < cfg.D_min) * 1.0
+        )
+        # Regulación
+        R_t = (
+            (S_t > cfg.S_max) * 1.0 +
+            (H_swarm > cfg.H_max) * 1.0 +
+            (delta_R < -cfg.eps_crash) * 1.0
+        )
+        self.T += cfg.eta_T * (E_t - R_t)
+        self.T = float(np.clip(self.T, cfg.T_min, cfg.T_max))
+        return float(E_t), float(R_t)
+
+    def _gate(self, H_swarm: float, S_t: float) -> float:
+        cfg = self.cfg
+        x = (
+            cfg.kT * (self.T - cfg.T_mid) +
+            cfg.kS * (S_t - cfg.S_mid) +
+            cfg.kH * (H_swarm - cfg.H_mid)
+        )
+        # sigmoide
+        w = 1.0 / (1.0 + math.exp(-x))
+        return float(w)
+
+    def act(self, s: Hashable, t: int):
+        """
+        Calcula la política STRANGE completa y samplea una acción.
+        Devuelve:
+            action, info_dict
+        """
+        # política del swarm y métricas internas
+        p_swarm, S_t, H_swarm, diversity = self._compute_swarm_policy(s, t)
+        self._update_proto(s, p_swarm)
+
+        # usamos ΔR del paso anterior para la termodinámica
+        delta_R = self.last_delta_R
+        E_t, R_t = self._thermo_step(delta_R, H_swarm, S_t, diversity)
+
+        # gate memoria vs swarm
+        w_t = self._gate(H_swarm, S_t)
+        proto_s = self.proto[s]
+        p_final = (1.0 - w_t) * p_swarm + w_t * proto_s
+        p_final = p_final / p_final.sum()
+
+        # sample acción
+        a = int(self.rng.choice(len(p_final), p=p_final))
+
+        info = {
+            "T": self.T,
+            "S_t": S_t,
+            "H_swarm": H_swarm,
+            "diversity": diversity,
+            "E_t": E_t,
+            "R_t": R_t,
+            "w_t": w_t,
+            "p_swarm": p_swarm,
+            "p_final": p_final,
+        }
+        return a, info
+
+    def update(
+        self,
+        s: Hashable,
+        a: int,
+        r: float,
+        s_next: Hashable,
+        done: bool,
+    ):
+        # Actualizamos Q de todos los agentes (paralelo tabular compartiendo experiencia)
+        for agent in self.agents:
+            agent.update(s, a, r, s_next, done)
+
+        # ΔR real respecto al paso anterior
+        delta_R = r - self.last_reward
+        self.last_reward = r
+        self.last_delta_R = delta_R
+
+        return delta_R
diff --git a/Strange/strange/results/strange_hypothesis_lab.csv b/Strange/strange/results/strange_hypothesis_lab.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5033653d4603c4e2822e0291fafc2d9bfae04ed1
--- /dev/null
+++ b/Strange/strange/results/strange_hypothesis_lab.csv
@@ -0,0 +1,10001 @@
+episode,step,reward,T,H_swarm,S_t,diversity,w_t,phase,progress,stability
+0,0,0.16239284849816465,0.75,1.3862943611198846,0.0007001722195526522,0.03633489525742447,0.4262163581318481,1,0.12867895136708699,0.23640422650443282
+0,1,0.20933495617024866,0.75,1.386281331431762,0.0007001717584162999,0.03673146826212116,0.42621317142129245,1,0.17332225763547587,0.3000201770535277
+0,2,0.17801207431534466,0.75,1.3862684578431035,0.000700154176952703,0.036747451665472435,0.4262100145180504,1,0.22279306726676829,0.26298282469545775
+0,3,0.18264295440323697,0.7,1.3862943611198846,0.0007001722195526527,0.03638937495624124,0.40798409882467046,1,0.2565603226413948,0.23339608448893018
+0,4,0.19903005490574757,0.75,1.3862772530362795,0.0007001723700085801,0.036663329245602116,0.4262121743266665,1,0.2743724060020693,0.23093697501639954
+0,5,0.20690411850521087,0.8,1.3862678456368887,0.0007001552885357025,0.03676404512539598,0.4446448750837256,1,0.2889298161734962,0.23777397345270798
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+0,8,0.29022787095346414,0.9000000000000001,1.386174993632612,0.0007001259206531205,0.03661655867294898,0.481901721653225,1,0.3836431261106656,0.3225687350306597
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+0,10,0.28974317569197466,1.0000000000000002,1.3859899091665442,0.0006999826121500312,0.036624207990221196,0.5193378180678181,1,0.416974112105275,0.3431784361662154
+0,11,0.2886679303091198,1.0500000000000003,1.3862943611198844,0.0007001722195526525,0.036728371598992,0.5380996785026854,1,0.45912070333033844,0.31673216325661463
+0,12,0.3115014021845327,1.1000000000000003,1.3862608119778679,0.0007001716097388504,0.03653024600999235,0.5566707315232401,1,0.47782456699341075,0.3345719179572279
+0,13,0.30800691138033337,1.1000000000000003,1.3862428179098751,0.0007001385295675733,0.03678566263451503,0.5566662744633148,1,0.5052339116723695,0.31971115570461855
+0,14,0.36292552387412297,1.1500000000000004,1.3861621423796564,0.0007000954829436889,0.03650844638549437,0.5750687806949815,1,0.5430470256793738,0.3523557120079116
+0,15,0.3516383815576207,1.1500000000000004,1.3861991040524406,0.0007002279219950206,0.03678549623211641,0.5750778775236854,1,0.5976272325147434,0.3419582951724113
+0,16,0.37897093784853514,1.2000000000000004,1.3860593139038158,0.0007002882970907765,0.03643243413340943,0.5932603136197275,1,0.6307782701620871,0.3555320992790008
+0,17,0.4219225461212837,1.2000000000000004,1.3862943611198844,0.0007001722195526524,0.03676137695795967,0.593316973843189,1,0.6607337288308862,0.3920968486297642
+0,18,0.47279262544647843,1.2000000000000004,1.386229093357471,0.0007001695942799111,0.03644447127741131,0.5933012238951778,1,0.7146849494327063,0.4563954855779865
+0,19,0.5040710458633327,1.2000000000000004,1.3862943611198844,0.0007001722195526523,0.036787506764947984,0.593316973843189,1,0.7528437697651345,0.5097784598575962
+0,20,0.4716099418636316,1.2000000000000004,1.3862010453637463,0.0007001725789834356,0.036386056969374574,0.5932944574805368,1,0.7811457506874058,0.49717213862889753
+0,21,0.47915010274505065,1.1500000000000004,1.3861673366976155,0.0007000836765301547,0.03676941601076256,0.5750700442321866,1,0.8244354469820078,0.4645864131741583
+0,22,0.5373316045618527,1.2000000000000004,1.3862943611198844,0.0007001722195526521,0.0367823004872644,0.593316973843189,1,0.8676390568528262,0.5009199394024074
+0,23,0.5605907790312219,1.2000000000000004,1.386188254967173,0.0007001692684897793,0.0367226103761662,0.5932913696058555,1,0.8981593465954251,0.5377568013101728
+0,24,0.5845851849423765,1.2000000000000004,1.3858467930158915,0.0007001548572381312,0.03666095648662952,0.5932089663776962,1,0.9219802761483491,0.585976914943456
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f50da50d625f87fedec8e9bcefb5c6ebc0332f3
--- /dev/null
+++ b/app.py
@@ -0,0 +1,368 @@
+import os
+import sys
+import time
+import uuid
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+ROOT = Path(__file__).resolve().parent
+
+# Make local quark folders importable
+for p in [ROOT / 'Down', ROOT / 'Strange', ROOT / 'Charm']:
+    if p.exists() and str(p) not in sys.path:
+        sys.path.insert(0, str(p))
+
+RUNTIME_DIR = Path('/tmp/twoquarks_runs')
+RUNTIME_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def _run_id() -> str:
+    return str(uuid.uuid4())[:8]
+
+
+def _save_csv(path: Path, header: list[str], rows: list[list[object]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open('w', encoding='utf-8') as f:
+        f.write(','.join(header) + '\n')
+        for row in rows:
+            f.write(','.join(map(str, row)) + '\n')
+
+
+def _plot_series(y, title, xlabel='step', ylabel='value'):
+    fig, ax = plt.subplots()
+    ax.plot(y)
+    ax.set_title(title)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.grid(True, alpha=0.2)
+    return fig
+
+
+# =========================
+# DOWN / AntiDown
+# =========================
+
+def run_down(episodes_per_phase: int, seed: int):
+    """Runs Down paradox tabular experiment (HFLevo vs LevoParadoxIsomer)."""
+    from down.exp.run_paradox_tabular import run_experiment
+
+    run_id = _run_id()
+    out_dir = RUNTIME_DIR / f'down_{run_id}'
+    out_csv = out_dir / 'down_paradox_tabular_results.csv'
+
+    run_experiment(out_csv=out_csv, episodes_per_phase=int(episodes_per_phase), seed=int(seed))
+
+    # Load and summarize
+    import csv
+    rows = []
+    with out_csv.open('r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for r in reader:
+            rows.append(r)
+
+    # Aggregate mean reward by phase+agent
+    phases = sorted({int(r['phase']) for r in rows})
+    agents = sorted({r['agent'] for r in rows})
+    summary = {}
+    for a in agents:
+        summary[a] = {p: [] for p in phases}
+    for r in rows:
+        summary[r['agent']][int(r['phase'])].append(float(r['episode_reward']))
+
+    table_lines = []
+    for a in agents:
+        for p in phases:
+            vals = summary[a][p]
+            table_lines.append({
+                'agent': a,
+                'phase': p,
+                'mean_reward': float(np.mean(vals)) if vals else float('nan'),
+                'std_reward': float(np.std(vals)) if vals else float('nan'),
+                'n': len(vals),
+            })
+
+    # Plot: mean reward per phase (bar-ish via line)
+    fig, ax = plt.subplots()
+    for a in agents:
+        means = [np.mean(summary[a][p]) if summary[a][p] else np.nan for p in phases]
+        ax.plot(phases, means, marker='o', label=a)
+    ax.set_title('DOWN: Mean reward by phase')
+    ax.set_xlabel('phase')
+    ax.set_ylabel('mean episode reward')
+    ax.grid(True, alpha=0.2)
+    ax.legend()
+
+    meta = {
+        'run_id': run_id,
+        'timestamp_utc': time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime()),
+        'seed': int(seed),
+        'episodes_per_phase': int(episodes_per_phase),
+        'artifact_csv': str(out_csv),
+    }
+
+    return meta, table_lines, fig, str(out_csv)
+
+
+def run_antidown(n_episodes: int, base_seed: int):
+    """Runs AntiDown corrupted valley tabular experiment with adjustable n_episodes."""
+    # Import pieces from the script
+    from AntiDown.envs.corrupted_valley import CorruptedValleyEnv
+    from AntiDown.levo.levo_q_tabular import LevoQTabularAgent
+    from AntiDown.levo.levo_thinking_ensemble import LevoThinkingEnsembleAgent
+    from AntiDown.exp.run_corrupted_valley_tabular import EpsGreedyQAgent, SoftmaxBoltzmannAgent, run_phase
+    from AntiDown.utils.logging import CSVLogger
+
+    run_id = _run_id()
+    out_dir = RUNTIME_DIR / f'antidown_{run_id}'
+    out_csv = out_dir / 'antidown_corrupted_valley_tabular.csv'
+
+    base_seed = int(base_seed)
+    seed_offset = 1000
+
+    probe_env = CorruptedValleyEnv(seed=base_seed, phase=1)
+    n_states = probe_env.n_states
+    n_actions = probe_env.n_actions
+
+    agents = [
+        EpsGreedyQAgent(n_states, n_actions, epsilon=0.1, name='EpsGreedy'),
+        SoftmaxBoltzmannAgent(n_states, n_actions, tau=0.5, name='Softmax'),
+        LevoQTabularAgent(n_states, n_actions, A=0.5, omega=0.05, ent_weight=0.0, name='LevoQ'),
+        LevoThinkingEnsembleAgent(n_states, n_actions, n_heads=5, A=0.5, omega=0.05, lambda_var=0.5, name='LevoThinking'),
+    ]
+
+    logger = CSVLogger(
+        str(out_csv),
+        fieldnames=['phase', 'phase_tag', 'episode', 'agent', 'total_reward', 'valley_visits'],
+    )
+
+    n_episodes = int(n_episodes)
+
+    # 3 phases
+    run_phase(base_seed, seed_offset, 1, n_episodes, agents, logger, noisy_heads_spec=None, noise_std=0.0)
+    run_phase(base_seed, seed_offset, 2, n_episodes, agents, logger, noisy_heads_spec=None, noise_std=0.0)
+    run_phase(base_seed, seed_offset, 3, n_episodes, agents, logger, noisy_heads_spec=None, noise_std=0.1)
+
+    # Summarize
+    import csv
+    rows = []
+    with out_csv.open('r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for r in reader:
+            rows.append(r)
+
+    phases = sorted({int(r['phase']) for r in rows})
+    agents_names = sorted({r['agent'] for r in rows})
+    summary = {a: {p: [] for p in phases} for a in agents_names}
+    for r in rows:
+        summary[r['agent']][int(r['phase'])].append(float(r['total_reward']))
+
+    table_lines = []
+    for a in agents_names:
+        for p in phases:
+            vals = summary[a][p]
+            table_lines.append({
+                'agent': a,
+                'phase': p,
+                'mean_total_reward': float(np.mean(vals)) if vals else float('nan'),
+                'std_total_reward': float(np.std(vals)) if vals else float('nan'),
+                'mean_valley_visits': float(np.mean([float(rr['valley_visits']) for rr in rows if rr['agent']==a and int(rr['phase'])==p])) if vals else float('nan'),
+                'n': len(vals),
+            })
+
+    fig, ax = plt.subplots()
+    for a in agents_names:
+        means = [np.mean(summary[a][p]) if summary[a][p] else np.nan for p in phases]
+        ax.plot(phases, means, marker='o', label=a)
+    ax.set_title('AntiDown: Mean total reward by phase')
+    ax.set_xlabel('phase')
+    ax.set_ylabel('mean episode total reward')
+    ax.grid(True, alpha=0.2)
+    ax.legend()
+
+    meta = {
+        'run_id': run_id,
+        'timestamp_utc': time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime()),
+        'base_seed': base_seed,
+        'episodes_per_phase': n_episodes,
+        'artifact_csv': str(out_csv),
+    }
+
+    return meta, table_lines, fig, str(out_csv)
+
+
+# =========================
+# STRANGE / AntiStrange
+# =========================
+
+def run_strange(n_episodes: int, max_steps: int, seed: int, mode: str):
+    if mode == 'Strange':
+        from strange.exp.run_strange_hypothesis_lab import run_experiment
+        out_name = 'strange_hypothesis_lab.csv'
+    else:
+        from AntiStrange.exp.run_antistrange_hypothesis_lab import run_antistrange as run_experiment
+        out_name = 'antistrange_hypothesis_lab.csv'
+
+    run_id = _run_id()
+    out_dir = RUNTIME_DIR / f"{mode.lower()}_{run_id}"
+    out_csv = out_dir / out_name
+
+    run_experiment(n_episodes=int(n_episodes), max_steps=int(max_steps), seed=int(seed), out_path=str(out_csv))
+
+    # Load CSV and make curves
+    import csv
+    rewards_by_ep = {}
+    with out_csv.open('r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+        for r in reader:
+            ep = int(r['episode'])
+            rewards_by_ep.setdefault(ep, 0.0)
+            rewards_by_ep[ep] += float(r['reward'])
+
+    eps = sorted(rewards_by_ep.keys())
+    ep_returns = [rewards_by_ep[e] for e in eps]
+
+    fig = _plot_series(ep_returns, f'{mode}: return per episode', xlabel='episode', ylabel='return')
+
+    metrics = {
+        'run_id': run_id,
+        'timestamp_utc': time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime()),
+        'mode': mode,
+        'seed': int(seed),
+        'n_episodes': int(n_episodes),
+        'max_steps': int(max_steps),
+        'return_mean_last_20': float(np.mean(ep_returns[-20:])) if len(ep_returns) >= 20 else float(np.mean(ep_returns)),
+        'return_std_last_20': float(np.std(ep_returns[-20:])) if len(ep_returns) >= 20 else float(np.std(ep_returns)),
+        'artifact_csv': str(out_csv),
+    }
+
+    return metrics, fig, str(out_csv)
+
+
+# =========================
+# CHARM
+# =========================
+
+def run_charm(n_episodes: int, seed: int):
+    from charm.levo.charm import train_charm_enchanted_valley
+
+    run_id = _run_id()
+    out_dir = RUNTIME_DIR / f'charm_{run_id}'
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    stats = train_charm_enchanted_valley(n_episodes=int(n_episodes), seed=int(seed))
+
+    rewards = stats['rewards']
+    lam = stats['lambda']
+    rho = stats['rho_mean']
+
+    fig_r = _plot_series(rewards, 'Charm: reward per episode', xlabel='episode', ylabel='reward')
+    fig_l = _plot_series(lam, 'Charm: lambda (meta-control) per episode', xlabel='episode', ylabel='lambda')
+    fig_rho = _plot_series(rho, 'Charm: rho_mean per episode', xlabel='episode', ylabel='rho_mean')
+
+    # Save CSV artifact
+    out_csv = out_dir / 'charm_timeseries.csv'
+    header = ['episode', 'reward', 'lambda', 'rho_mean']
+    rows = [[i, float(rewards[i]), float(lam[i]), float(rho[i])] for i in range(len(rewards))]
+    _save_csv(out_csv, header, rows)
+
+    metrics = {
+        'run_id': run_id,
+        'timestamp_utc': time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime()),
+        'seed': int(seed),
+        'n_episodes': int(n_episodes),
+        'reward_mean_last_50': float(np.mean(rewards[-50:])) if len(rewards) >= 50 else float(np.mean(rewards)),
+        'reward_std_last_50': float(np.std(rewards[-50:])) if len(rewards) >= 50 else float(np.std(rewards)),
+        'artifact_csv': str(out_csv),
+    }
+
+    return metrics, fig_r, fig_l, fig_rho, str(out_csv)
+
+
+# =========================
+# UI
+# =========================
+
+def build_ui():
+    with gr.Blocks(title='TwoQuarks — QuarksLab (Interactive)') as demo:
+        gr.Markdown(
+            """# TwoQuarks • QuarksLab (Interactive)\n\n"
+            "Real runs, real knobs: seed/episodes/steps. Short-bounded experiments (public CPU)."
+            """
+        )
+
+        with gr.Tab('DOWN / AntiDown'):
+            gr.Markdown('Tabular experiments (fast).')
+            with gr.Row():
+                down_eps = gr.Slider(50, 800, value=200, step=50, label='DOWN: episodes_per_phase')
+                down_seed = gr.Number(value=2025, precision=0, label='DOWN: seed')
+                run_down_btn = gr.Button('Run DOWN')
+            down_meta = gr.JSON(label='DOWN: run meta')
+            down_table = gr.Dataframe(label='DOWN: summary (mean/std by phase+agent)', interactive=False)
+            down_plot = gr.Plot(label='DOWN: mean reward by phase')
+            down_file = gr.File(label='DOWN: results CSV')
+
+            run_down_btn.click(
+                fn=run_down,
+                inputs=[down_eps, down_seed],
+                outputs=[down_meta, down_table, down_plot, down_file],
+            )
+
+            gr.Markdown('---')
+            with gr.Row():
+                ad_eps = gr.Slider(20, 400, value=120, step=20, label='AntiDown: episodes_per_phase')
+                ad_seed = gr.Number(value=1234, precision=0, label='AntiDown: base seed')
+                run_ad_btn = gr.Button('Run AntiDown')
+            ad_meta = gr.JSON(label='AntiDown: run meta')
+            ad_table = gr.Dataframe(label='AntiDown: summary', interactive=False)
+            ad_plot = gr.Plot(label='AntiDown: mean total reward by phase')
+            ad_file = gr.File(label='AntiDown: results CSV')
+
+            run_ad_btn.click(
+                fn=run_antidown,
+                inputs=[ad_eps, ad_seed],
+                outputs=[ad_meta, ad_table, ad_plot, ad_file],
+            )
+
+        with gr.Tab('STRANGE / AntiStrange'):
+            gr.Markdown('HypothesisLab: swarm dynamics with hidden regime shifts.')
+            mode = gr.Radio(['Strange', 'AntiStrange'], value='Strange', label='Mode')
+            with gr.Row():
+                seps = gr.Slider(50, 500, value=200, step=50, label='Episodes')
+                ssteps = gr.Slider(10, 120, value=50, step=5, label='Max steps per episode')
+                sseed = gr.Number(value=0, precision=0, label='Seed')
+            run_s_btn = gr.Button('Run')
+            s_meta = gr.JSON(label='Run meta')
+            s_plot = gr.Plot(label='Return per episode')
+            s_file = gr.File(label='Results CSV')
+            run_s_btn.click(fn=run_strange, inputs=[seps, ssteps, sseed, mode], outputs=[s_meta, s_plot, s_file])
+
+        with gr.Tab('CHARM'):
+            gr.Markdown('Enchanted Valley: non-stationary graph + CharmField meta-control.')
+            with gr.Row():
+                ceps = gr.Slider(50, 600, value=300, step=50, label='Episodes')
+                cseed = gr.Number(value=0, precision=0, label='Seed')
+                run_c_btn = gr.Button('Run CHARM')
+            c_meta = gr.JSON(label='Run meta')
+            c_plot_r = gr.Plot(label='Reward')
+            c_plot_l = gr.Plot(label='Lambda')
+            c_plot_rho = gr.Plot(label='Rho_mean')
+            c_file = gr.File(label='Timeseries CSV')
+            run_c_btn.click(fn=run_charm, inputs=[ceps, cseed], outputs=[c_meta, c_plot_r, c_plot_l, c_plot_rho, c_file])
+
+        gr.Markdown(
+            """### Notes
+- This Space runs bounded experiments on shared CPU.
+- For heavy runs, keep episodes modest and use the CSV artifact to reproduce locally.
+"""
+        )
+
+    return demo
+
+
+demo = build_ui()
+
+if __name__ == '__main__':
+    demo.launch()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..760bcae49e34b867422083a41b21d3c651901a74
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+gradio
+numpy
+matplotlib