upload batch_0602/typeI tasks

bns_merger_disk_ejecta__Mdisk/metadata.yaml

@@ -53,7 +53,8 @@ inputs:
     train: [116, 1439]
     test:  [208, 1726.28]
 
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 n_train: 52
 n_test: 76

bns_merger_disk_ejecta__Mej/metadata.yaml

@@ -68,7 +68,8 @@ inputs:
     test:  [340.802, 1437.16]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 n_train: 50
 n_test: 54

bns_merger_disk_ejecta__vej/metadata.yaml

@@ -54,7 +54,8 @@ inputs:
     test:  [0.103, 0.150939]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 n_train: 207
 n_test: 35

cloud_cover_parameterization__cloud_cover/metadata.yaml

@@ -76,7 +76,8 @@ inputs:
       test:  [-0.002824, 0.000589]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv  # random 80% of the NARVAL-II R2B4 snapshot (seed=42)

exfor_neutron_capture_resonance_gold__sigma_E/metadata.yaml

@@ -13,7 +13,7 @@ target:
   name: log10_sigma_b
   symbol: log10(sigma)
   unit: log10(barn)
-  description: Base-10 logarithm of the Au-197(n,gamma) inclusive capture cross section in barns.  The log transform is needed because sigma spans >5 decades from the thermal background (~10-100 b) to the 4.9 eV resonance peak (~33,000 b), measured in the E < 50 eV window.
+  description: Base-10 logarithm of the Au-197(n,gamma) inclusive capture cross section in barns.  The log transform is needed because sigma spans >5 decades from the thermal background (~10-100 b) to the resonance peak, measured in the E < 50 eV window.
   range:
     train: [-0.866238, 4.44623]
     test:  [-1.18417, 4.3353]

ghg_emission_drivers__CDE/metadata.yaml

@@ -62,7 +62,8 @@ inputs:
       test:  [-4.96912, 28.9267]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv   # GDP <= 20000 USD/cap (lower/middle-income regime)

ghg_emission_drivers__EC/metadata.yaml

@@ -61,7 +61,8 @@ inputs:
       train: [-4.90051, 29.7776]
       test:  [-20.4459, 28.9267]
 
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv  # rows with GDP <= 75th percentile (lower 75% of income distribution)

ghg_emission_drivers__ME/metadata.yaml

@@ -62,7 +62,8 @@ inputs:
       test:  [-4.96912, 28.9267]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv   # GDP <= 20000 USD/cap (lower/middle-income regime)

ghg_emission_drivers__NOE/metadata.yaml

@@ -62,7 +62,8 @@ inputs:
       test:  [-4.96912, 28.9267]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv   # GDP <= 20000 USD/cap (lower/middle-income regime)

ground_motion_ita18__log10_pga/metadata.yaml

@@ -88,7 +88,8 @@ inputs:
     categories: [0, 1]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv  # Mw <= 5.7 (small-to-moderate regime, 3449 rows)

hacks_law_river_length_hydrosheds__main_length_km/metadata.yaml

@@ -100,7 +100,8 @@ n_test: 7085
 #    harness evaluator; 15/16 batch02 tasks use rmse). See VERDICT.md.
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block kept for schema symmetry.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 references:
 - id: hack_1957

island_species_richness_terceira__S_terceira/metadata.yaml

@@ -66,7 +66,8 @@ n_train: 34
 n_test:  18
 
 # Candidate prior constants — the `priors` prompt slot.
-priors: []   # no candidate constants offered for this task
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []   # no candidate constants offered for this task
 
 # Reference-baseline bank
 references:

lake_thermocline_depth_pilla__z_t/metadata.yaml

@@ -76,7 +76,8 @@ n_test:  245
 # constants cannot memorise 126 lakes). A lake-grouped A_s-stratified split was
 # considered and deferred as optional rigor.
 
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 # FM C12 fix (2026-05-26): removed patalas_exponent=0.205 (boehrer_2008.py LAW
 # b_patalas) and patalas_coefficient=4.6 (boehrer_2008.py LAW a_patalas). Both
 # are empirical regression coefficients from Patalas (1984) calibrated on a sample

neo_size_frequency_distribution__N_cum_H/metadata.yaml

@@ -13,7 +13,7 @@ target:
   name: N_cum_H
   symbol: N(<H)
   unit: ""
-  description: Cumulative count of near-Earth objects with absolute magnitude at or brighter than the bin's upper edge (count of catalog NEOs with H <= H_upper).  Spans the H in [12,18] window; the declared metric is log_mae (the SFD is exponential, spanning ~3 decades in count).
+  description: Cumulative count of near-Earth objects with absolute magnitude at or brighter than the bin's upper edge (count of catalog NEOs with H <= H_upper).  Spans the H in [12,18] window; the declared metric is log_mae.
   range:
     train: [2, 1097]
     test:  [11, 760]

nuclear_binding_energy_ame2020__BE_per_A/data/test.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:097d314c0491773eca6f974923ca1948108bc30c4226448bc0b407fe79364cd1
-size 21822
+oid sha256:5c37c3c058f705194801cd4886b015d3f29a50a500bc37a37f9678e0f278780b
+size 11936

nuclear_binding_energy_ame2020__BE_per_A/data/train.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cb96ef46387d80df07a8e1e0ab0cd6cde00bef91584e2958ac68d7f615ca3c24
-size 90741
+oid sha256:036d11613fba39f65b41999213153c0c5b536ff901a1fe2c69890ce93c6d1d47
+size 49141

nuclear_binding_energy_ame2020__BE_per_A/metadata.yaml

@@ -49,20 +49,6 @@ inputs:
     range:
       train: [-5, 51]
       test:  [18, 59]
-  - name: asym
-    symbol: I
-    unit: dimensionless
-    description: Isospin asymmetry, (N - Z) / A.
-    range:
-      train: [-0.5, 0.666667]
-      test:  [0.096774, 0.253219]
-  - name: coul_proxy
-    symbol: Z(Z-1)/A^(1/3)
-    unit: dimensionless
-    description: Coulomb proxy, Z(Z-1) / A^(1/3) — proportional to electrostatic self-energy.
-    range:
-      train: [0, 1180.75]
-      test:  [1130.85, 1857.38]
   - name: pair
     symbol: delta
     unit: categorical

nuclear_binding_energy_ame2020__BE_per_A/prep_data.py

@@ -15,14 +15,19 @@ Column 1  Z          : int         — proton number (atomic number)
 Column 2  N          : int         — neutron number
 Column 3  A          : int         — mass number (= Z + N; redundant but canonical)
 Column 4  NZ_diff    : int         — neutron excess N − Z
-Column 5  asym       : float       — isospin asymmetry I = (N − Z) / A
-Column 6  coul_proxy : float       — Coulomb proxy Z(Z−1) / A^(1/3)
-Column 7  pair       : int         — parity indicator (+1 even-even, 0 odd-A, −1 odd-odd)
+Column 5  pair       : int         — parity indicator (+1 even-even, 0 odd-A, −1 odd-odd)
 
 Dropped from working CSV:
   'element' — element symbol, 1-to-1 with Z. Leaks provenance and allows
   SR methods to memorise per-element patterns without learning the underlying
   physics. The benchmark tests formula generalisation, not element lookup.
+  'asym' = (N−Z)/A and 'coul_proxy' = Z(Z−1)/A^(1/3) — pre-engineered
+  liquid-drop terms. These hand the SR system the answer's functional
+  structure (the asymmetry term and, especially, the non-trivial Coulomb
+  term Z(Z−1)/A^(1/3)), reducing discovery to coefficient fitting. No
+  reference baseline consumes them (all derive their terms from raw
+  Z,N,A,NZ_diff,pair). Dropped 2026-06-13 to keep this a genuine
+  discovery task.
 
 === TYPE I vs TYPE II DECISION ===
 Verdict: TYPE I (no group_id column).
@@ -107,9 +112,9 @@ DATA_DIR = TASK_DIR / "data"
 EXPECTED_SHA256 = "0907c78d0626ca265f3d515c421d8e8360c0de7f32ac846079197da3554cd623"
 EXPECTED_ROWS   = 2548
 
-OUT_COLS  = ["BE_per_A", "Z", "N", "A", "NZ_diff", "asym", "coul_proxy", "pair"]
+OUT_COLS  = ["BE_per_A", "Z", "N", "A", "NZ_diff", "pair"]
 INT_COLS  = ["Z", "N", "A", "NZ_diff", "pair"]
-FLOAT_COLS = ["BE_per_A", "asym", "coul_proxy"]
+FLOAT_COLS = ["BE_per_A"]
 
 
 def main() -> None:

proton_em_form_factor__GE_over_GD/metadata.yaml

@@ -29,7 +29,8 @@ inputs:
       test:  [2.07, 5.85]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 # Dataset — Type I two-file layout
 data_files:

protoplanetary_disk_mmflux__F_mm/metadata.yaml

@@ -52,7 +52,8 @@ inputs:
       train: [-1.57, 1.7]
       test:  [-0.48, 1.29]
 
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv   # low/mid-mass T Tauri stars, log10_M_star <= -0.3 (~0.5 M_sun)

red_giant_asteroseismology__delta_nu/metadata.yaml

@@ -15,7 +15,7 @@ target:
   name: delta_nu
   symbol: Δν
   unit: uHz
-  description: Large frequency separation between consecutive radial-order p-mode oscillations in a red-giant power spectrum, measured by autocorrelating the power spectrum (Yu+2018 §3.2).
+  description: Large frequency separation between consecutive radial-order p-mode oscillations in a red-giant power spectrum, measured by autocorrelating the power spectrum.
   range:
     train: [0.747, 9.219]
     test:  [7.139, 19.294]
@@ -24,7 +24,7 @@ inputs:
   - name: nu_max
     symbol: ν_max
     unit: uHz
-    description: Frequency of maximum oscillation power, measured directly from the Gaussian fit to the power-spectrum envelope (Yu+2018 §3.1). Independent of delta_nu.
+    description: Frequency of maximum oscillation power, measured directly from the Gaussian fit to the power-spectrum envelope. Independent of delta_nu.
     range:
       train: [3.97, 92.86]
       test:  [92.88, 273.16]

smbh_mass_sigma_relation__log_M_BH/metadata.yaml

@@ -128,7 +128,8 @@ n_test: 25
 # 5. Metric: rmse (the evaluate.py harness computes rmse/mae/mse/smape/r2).
 
 # Candidate prior constants — the `priors` prompt slot.
-priors: []   # OTHER pivots deferred (Phase-2); no candidate constants offered yet
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []   # OTHER pivots deferred (Phase-2); no candidate constants offered yet
 
 references:
   # measured blocks computed 2026-05-29 by evaluate.py reference on the

spirometry_nhanes__FEV1_L/metadata.yaml

@@ -51,7 +51,8 @@ inputs:
 # Empty: no leak-free numeric prior is natural for this task (the b0..b3
 # coefficients are the LAW recovery target; there are no auxiliary physical
 # constants to offer). Present for schema symmetry with the GOLD exemplar.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 data_files:
   train: data/train.csv

spirometry_nhanes__FVC_L/metadata.yaml

@@ -70,7 +70,8 @@ references:
 #   dropped from inputs (constant within the stratum). See VERDICT.md.
 # priors intentionally empty: the baseline is an empirical regression with no fundamental
 #   physical constants; exposing its fit coefficients as priors would leak the answer (FM-H1/C12).
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 caps:
   max_law_constants: 4

trade_gravity_cepii__log_tradeflow/metadata.yaml

@@ -77,7 +77,8 @@ n_test: 109477
 # challenge the stability of gravity calibrations from the pre-disruption era.
 
 # Candidate prior constants — the `priors` prompt slot.
-priors: []   # no candidate constants offered for this task
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []   # no candidate constants offered for this task
 
 # Reference-baseline bank
 references:

ttauri_accretion__L_acc/metadata.yaml

@@ -51,7 +51,8 @@ inputs:
       test:  [2600, 5100]
 
 # FM-H3 backfill (wave-11, 2026-05-26): empty priors block added for schema symmetry with GOLD.
-priors: []
+# priors DISABLED 2026-06-08 — not shown to the agent (redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
+# priors: []
 
 # Dataset — Type I two-file layout. CROSS-REGION OOD split (2026-05-29):
 #   train = Chamaeleon I  (Manara 2017, A&A 604 A127; data_raw/manara2017_chai_accretion.csv)

volcanic_column_mer_ivespa__H_top/metadata.yaml

@@ -13,7 +13,7 @@ target:
   name: H_top_km_avl
   symbol: H_top
   unit: km
-  description: Time-averaged eruption-column top height in km above vent level (Tephra Plume Top a.s.l. minus vent altitude).  The declared metric is log_mae (the relationship is a power law with multiplicative ~53% scatter, so log-space error is natural; H_top is strictly positive).
+  description: Time-averaged eruption-column top height in km above vent level (Tephra Plume Top a.s.l. minus vent altitude).  The declared metric is log_mae.
   range:
     train: [0.585, 17.25]
     test:  [5.392, 36.415]

wind_turbine_power_curve_engie__power_kW/data/test.csv

@@ -1,3 +1,3 @@
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-oid sha256:327ab083170926bd9751b706c1c2771e762f408fd2018c14136b5bcd76eab77b
-size 1586936
+oid sha256:0d978c8794ceb2a075b7feebf5837f1d916e165312c097619335b304d400b72c
+size 2018351

wind_turbine_power_curve_engie__power_kW/data/train.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:26208627dac0f6500718ecd3ab0f6695afc6d13fa92a6a42ab31a86c16cf0adc
-size 2399434
+oid sha256:9ec96341d396c3208e57a7f15622f53ae1e2f267ee75e3cdf7a0b6df06c5d787
+size 1968019

wind_turbine_power_curve_engie__power_kW/formulas/carrillo_2013_cubic.py

@@ -37,7 +37,7 @@ LAW_CONSTANTS — frozen
 - V_R    = 14.0    (Senvion MM82 datasheet)
 - V_CO   = 25.0    (Senvion MM82 datasheet)
 - P_R    = 2050.0  (Senvion MM82 datasheet)
-- CP_EQ  = 0.4249  (pre-fit on v2 train cubic regime)
+- CP_EQ  = 0.4073  (pre-fit on v2 train (2014) cubic regime)
 
 OTHER_CONSTANTS — universal physics
 -----------------------------------
@@ -56,7 +56,7 @@ LAW_CONSTANTS = {
     "V_R":   14.0,
     "V_CO":  25.0,
     "P_R":   2050.0,
-    "CP_EQ": 0.4249,
+    "CP_EQ": 0.4073,
 }
 OTHER_CONSTANTS = {
     "RHO": 1.225,
@@ -67,7 +67,7 @@ LOCAL_FITTABLE = {}
 
 def predict(X: np.ndarray,
             V_CI: float = 3.5, V_R: float = 14.0, V_CO: float = 25.0,
-            P_R: float = 2050.0, CP_EQ: float = 0.4249) -> np.ndarray:
+            P_R: float = 2050.0, CP_EQ: float = 0.4073) -> np.ndarray:
     """Piecewise cubic Betz power curve."""
     v = np.asarray(X[:, 0], dtype=float)
     rho = OTHER_CONSTANTS["RHO"]

wind_turbine_power_curve_engie__power_kW/formulas/lydia_2014_4pl.py

@@ -17,16 +17,15 @@ better to the saturation regime, as the v2 train/test split confirms.
 The paper publishes NO universal (a, m, n, tau) — these are
 turbine-specific fits.  For this Type I baseline, `a` is frozen to
 the Senvion MM82 datasheet rated power (2050 kW), and (m, n, tau)
-are pre-fit on the v2 TRAIN wind-speed band (4.69 - 7.48 m/s, 204k
+are pre-fit on the v2 TRAIN year (2014, full wind-speed range, 169k
 rows).
 
 LAW_CONSTANTS — frozen
 ----------------------
-- A    =  2050.0   (Senvion MM82 datasheet rated power, kW)
-- M    = -10.0     (pre-fit on v2 train; hits constrained bound but
-                    stable across multi-start)
-- N    =  114.51   (pre-fit on v2 train)
-- TAU  =   1.7212  (pre-fit on v2 train, m/s)
+- A    =  2050.0    (Senvion MM82 datasheet rated power, kW)
+- M    =  -7.4838   (pre-fit on v2 train (2014))
+- N    =  113.6020  (pre-fit on v2 train (2014))
+- TAU  =   1.7765   (pre-fit on v2 train (2014), m/s)
 
 OTHER_CONSTANTS / LOCAL_FITTABLE
 --------------------------------
@@ -45,16 +44,16 @@ EQUATION_LOC = (
 
 LAW_CONSTANTS = {
     "A":    2050.0,
-    "M":   -10.0,
-    "N":    114.5147,
-    "TAU":  1.7212,
+    "M":   -7.4838,
+    "N":    113.6020,
+    "TAU":  1.7765,
 }
 OTHER_CONSTANTS = {}
 LOCAL_FITTABLE = {}
 
 
-def predict(X: np.ndarray, A: float = 2050.0, M: float = -10.0,
-            N: float = 114.5147, TAU: float = 1.7212) -> np.ndarray:
+def predict(X: np.ndarray, A: float = 2050.0, M: float = -7.4838,
+            N: float = 113.6020, TAU: float = 1.7765) -> np.ndarray:
     """P = A · (1 + M·exp(-u/TAU)) / (1 + N·exp(-u/TAU))."""
     u = np.asarray(X[:, 0], dtype=float)
     expo = np.exp(-u / TAU)

wind_turbine_power_curve_engie__power_kW/formulas/reference_metrics.json

@@ -6,7 +6,7 @@
       "failed": false,
       "kind": "reference",
       "law_constants": {
-        "CP_EQ": 0.4249,
+        "CP_EQ": 0.4073,
         "P_R": 2050.0,
         "V_CI": 3.5,
         "V_CO": 25.0,
@@ -14,15 +14,15 @@
       },
       "local_fittable": [],
       "metrics": {
-        "log_mae": 15.365110605795655,
-        "mae": 127.90331999910883,
-        "mape": 1.3183523175020733,
-        "mdae": 62.67779329794564,
-        "mse": 54907.94590102975,
-        "n_finite": 136192,
-        "r2": 0.8565494912632164,
-        "rmse": 234.32444580331295,
-        "smape": 0.5089855363071308
+        "log_mae": 5.655283637677601,
+        "mae": 100.51001411608006,
+        "mape": 0.6549132665989366,
+        "mdae": 55.47323395614518,
+        "mse": 31129.672769289187,
+        "n_finite": 171525,
+        "r2": 0.8610131784992723,
+        "rmse": 176.43603024691183,
+        "smape": 0.32695142641721187
       },
       "other_constants": {
         "A": 5281.0,
@@ -37,21 +37,21 @@
       "kind": "reference",
       "law_constants": {
         "A": 2050.0,
-        "M": -10.0,
-        "N": 114.5147,
-        "TAU": 1.7212
+        "M": -7.4838,
+        "N": 113.602,
+        "TAU": 1.7765
       },
       "local_fittable": [],
       "metrics": {
-        "log_mae": 48.07096719703482,
-        "mae": 60.61008983902672,
-        "mape": 2.6794155156059616,
-        "mdae": 41.79090542383808,
-        "mse": 7951.817420926642,
-        "n_finite": 136192,
-        "r2": 0.9792253701045379,
-        "rmse": 89.17296350871514,
-        "smape": 0.5724634608408323
+        "log_mae": 6.926038218367585,
+        "mae": 47.43964117771709,
+        "mape": 0.674180961616683,
+        "mdae": 31.583427674249947,
+        "mse": 5331.526213520674,
+        "n_finite": 171525,
+        "r2": 0.9761959630074849,
+        "rmse": 73.0173007822165,
+        "smape": 0.22116833073300837
       },
       "other_constants": {},
       "paper_ref": "summary_formula_lydia_2014.md"
@@ -64,7 +64,7 @@
     "max_local_params": 0
   },
   "metric_declared": "rmse",
-  "n_test_rows": 136192,
+  "n_test_rows": 171525,
   "reference_baseline_id": null,
   "task": "wind_turbine_power_curve_engie__power_kW",
   "type": "typeI"

wind_turbine_power_curve_engie__power_kW/metadata.yaml

@@ -15,7 +15,7 @@ target:
   unit: kW
   description: 10-minute-averaged active electrical power output of the wind turbine.
   range:
-    train: [0, 1376.32]
+    train: [0, 2047.73]
     test:  [0, 2051.87]
 
 inputs:
@@ -24,22 +24,35 @@ inputs:
     unit: m/s
     description: 10-minute-averaged wind speed at hub height, measured by the turbine's nacelle anemometer.
     range:
-      train: [4.69, 7.48]
+      train: [0, 16.95]
       test:  [0, 19.31]
 
 # Dataset — Type I, two-file flat layout.  Source: ENGIE La Haute Borne
 # 2014-2015 SCADA archive (data_raw/la-haute-borne-data-2014-2015.csv,
 # 420k 10-min rows for 4 turbines R80711/R80721/R80736/R80790, all
-# Senvion MM82 2 MW machines).  Filters: drop rows with NaN in P or v,
-# and drop rows with P < 0 (curtailment / motor-power events).  Pooled
-# across 4 turbines as homogeneous power-curve-law evidence (340k rows
-# after filter); split by wind speed quantile (middle 60% train,
-# extremes 20%+20% test) to test wind-speed-extrapolation OOD.
+# Senvion MM82 2 MW machines).  Filters: drop rows with NaN in
+# Date_time/P/v, and drop rows with P < 0 (curtailment / motor-power
+# events).  Pooled across 4 turbines as homogeneous power-curve-law
+# evidence.  Split is TEMPORAL-GENERALIZATION OOD: train = calendar
+# year 2014, test = calendar year 2015 (+ 2016-01-01 rollover rows).
+# Both years span the full wind-speed range, so cut-in, the cubic Betz
+# climb, rated saturation, and cut-out are all present in train — the
+# full power-curve law is discoverable, and the test probes year-to-year
+# generalization (seasonal wind conditions, turbine aging).
+#
+# WHY NOT wind-speed-extrapolation OOD (rejected 2026-06-13): a
+# "middle-wind-speed train / extreme test" split hid the cut-in, the
+# rated asymptote, and the cut-out entirely from train, making the rated
+# power and cut thresholds physically unlearnable (impossible
+# extrapolation; SR submissions scored ~0). The old baselines only
+# "won" by injecting the datasheet rated power as a frozen constant the
+# SR system never saw. The temporal split exposes the full curve in
+# train.
 data_files:
   train: data/train.csv
   test:  data/test.csv
-n_train: 204288
-n_test:  136192
+n_train: 168955
+n_test:  171525
 
 # Candidate prior constants — the `priors` prompt slot.
 # priors DISABLED 2026-06-08 — not shown to the agent (test showed they're redundant: models already know the constants / the answer is fittable from data). Kept commented for record.
@@ -72,7 +85,7 @@ references:
     n_law_constants: 5         # V_CI, V_R, V_CO, P_R, CP_EQ
     n_other_constants: 2       # RHO, A
     n_local_params: 0
-    measured: {rmse: 234.36, r2: 0.8565}
+    measured: {rmse: 176.44, r2: 0.8610}
   - id: lydia_2014_4pl
     label: Lydia et al. (2014) 4-parameter logistic (Type I, frozen LAW_CONSTANTS)
     formula_file: formulas/lydia_2014_4pl.py
@@ -80,7 +93,7 @@ references:
     n_law_constants: 4         # A, M, N, TAU
     n_other_constants: 0
     n_local_params: 0
-    measured: {rmse: 89.16, r2: 0.9792}
+    measured: {rmse: 73.02, r2: 0.9762}
 
 # === v2 anti-dump caps ===
 caps:
@@ -102,9 +115,8 @@ validity_rubrics:
   - "predicted power is zero below the cut-in wind speed"
   - "predicted power is non-decreasing from cut-in wind speed up to rated wind speed"
   - "predicted power never exceeds the Betz-limited kinetic power available through the rotor"
-  - "predicted power never exceeds the turbine rated power"
   - "predicted power is zero above the cut-out wind speed"
 
 # === Scoring ===
 metric: rmse              # RMS error on power_kW
-best_baseline: 89.16     # lydia_2014_4pl
+best_baseline: 73.02     # lydia_2014_4pl (temporal 2014→2015 split)

wind_turbine_power_curve_engie__power_kW/prep_data.py

@@ -24,20 +24,30 @@ physics (Betz/cubic cascade), not specific to a single turbine.
 Demoted to **Type I** on 2026-05-24: pool all 4 turbines, wind-speed
 extrapolation OOD.
 
-Task framing — Type I, wind-speed-extrapolation OOD
----------------------------------------------------
+Task framing — Type I, temporal-generalization OOD
+--------------------------------------------------
 Predict 10-min-averaged active power output `power_kW` from
 hub-height wind speed `wind_speed_mps`.  Bank baselines have all
 parameters (cut-in, rated, cut-out, Cp, etc.) frozen as LAW_CONSTANTS,
 either from published turbine datasheets (Senvion MM82) or pre-fit on
-the train wind-speed band.  The test asks whether laws calibrated on
-middle wind speeds (4.7 - 7.5 m/s, where the cubic Betz regime
-dominates) extrapolate correctly to (a) sub-cut-in low speeds (zero
-power regime) and (b) high/saturating speeds approaching rated power
-(2 MW).
+the train year.  The test asks whether a power-curve law calibrated on
+one calendar year (2014) generalizes to the next (2015) under
+different seasonal wind conditions and turbine aging.
+
+  WHY NOT wind-speed-extrapolation OOD (rejected 2026-06-13): the
+  power curve is piecewise with three regime changes (cut-in, rated
+  saturation, cut-out).  A "middle-wind-speed train, extreme-wind-speed
+  test" split hides the cut-in, the rated asymptote, and the cut-out
+  entirely from train, so the asymptote (rated power) and the cut
+  thresholds are physically unlearnable from train — an impossible
+  extrapolation.  The old baselines only "won" because they injected
+  the datasheet rated power (2050 kW) as a frozen constant the SR
+  system never gets to see.  A temporal split exposes the FULL curve
+  (all three regimes) in train, making the law genuinely discoverable
+  while still testing year-to-year generalization.
 
 Filters applied:
-  (i)   Drop rows with missing P_avg or Ws_avg.
+  (i)   Drop rows with missing Date_time, P_avg or Ws_avg.
   (ii)  Drop rows with P_avg < 0 (curtailment / motor-power-consumption
         events; the power curve law describes generation, not
         curtailment).
@@ -47,18 +57,18 @@ Canonical column projection
 Output (col 0): power_kW = P_avg.
 Inputs (cols 1+): wind_speed_mps = Ws_avg.
 
-Split — wind-speed-extrapolation OOD
-------------------------------------
-Sort by wind_speed_mps; train = middle 60% (quantile 0.2-0.8, mostly
-the cubic-Betz regime ~4.7-7.5 m/s), test = extremes (bottom 20% +
-top 20%, including both the sub-cut-in zero-power regime and the
-near-rated 2 MW saturation regime).
+Split — temporal-generalization OOD
+-----------------------------------
+train = calendar year 2014, test = calendar year 2015 (the handful of
+2016-01-01 rollover rows are pooled into test).  All four turbines are
+pooled in both splits.  Both years span the full wind-speed range, so
+cut-in, the cubic Betz climb, rated saturation, and cut-out are all
+present in train.
 """
 
 import sys
 import hashlib
 import pathlib
-import numpy as np
 import pandas as pd
 
 HERE    = pathlib.Path(__file__).resolve().parent
@@ -66,33 +76,28 @@ SRC     = HERE / "data_raw" / "la-haute-borne-data-2014-2015.csv"
 OUT_DIR = HERE / "data"
 OUT_DIR.mkdir(exist_ok=True)
 
-TRAIN_QUANTILE_LO = 0.20
-TRAIN_QUANTILE_HI = 0.80
-
 
 def load_data(src: pathlib.Path) -> pd.DataFrame:
-    df = pd.read_csv(src, low_memory=False, usecols=["P_avg", "Ws_avg"])
-    df = df.dropna(subset=["P_avg", "Ws_avg"]).copy()
+    df = pd.read_csv(src, low_memory=False,
+                     usecols=["Date_time", "P_avg", "Ws_avg"])
+    df = df.dropna(subset=["Date_time", "P_avg", "Ws_avg"]).copy()
     df = df[df["P_avg"] >= 0].copy()  # drop curtailment / motor events
     assert len(df) >= 100_000, f"Expected >=100k rows, got {len(df)}"
     return df
 
 
 def build_released_df(df: pd.DataFrame) -> pd.DataFrame:
-    df = df.sort_values("Ws_avg").reset_index(drop=True)
     return pd.DataFrame({
-        "power_kW":       df["P_avg"].astype(float).round(2),
-        "wind_speed_mps": df["Ws_avg"].astype(float).round(3),
+        "power_kW":       df["P_avg"].astype(float).round(2).values,
+        "wind_speed_mps": df["Ws_avg"].astype(float).round(3).values,
+        "_year":          df["Date_time"].astype(str).str[:4].values,
     })
 
 
-def split_two_segments(released: pd.DataFrame):
-    """Wind-speed-extrapolation OOD: middle 60% train, extremes 20%+20% test."""
-    n = len(released)
-    lo = int(np.floor(n * TRAIN_QUANTILE_LO))
-    hi = int(np.ceil(n * TRAIN_QUANTILE_HI))
-    train = released.iloc[lo:hi].copy()
-    test  = pd.concat([released.iloc[:lo], released.iloc[hi:]], axis=0).copy()
+def split_by_year(released: pd.DataFrame):
+    """Temporal-generalization OOD: train = 2014, test = 2015 (+2016 rollover)."""
+    train = released[released["_year"] == "2014"].drop(columns="_year").copy()
+    test  = released[released["_year"] != "2014"].drop(columns="_year").copy()
     return train, test
 
 
@@ -107,7 +112,7 @@ def md5(path: pathlib.Path) -> str:
 def main() -> int:
     raw = load_data(SRC)
     released = build_released_df(raw)
-    train, test = split_two_segments(released)
+    train, test = split_by_year(released)
 
     paths = {}
     for name, d in [("train", train), ("test", test)]:
@@ -116,11 +121,11 @@ def main() -> int:
         paths[name] = p
 
     print("Written:")
-    print(f"  train.csv = {len(train)} rows (middle 60% by wind speed)")
-    print(f"  test.csv  = {len(test)} rows (extremes: bottom 20% + top 20%)")
-    print(f"  train ws range:  {train['wind_speed_mps'].min():.2f} - {train['wind_speed_mps'].max():.2f} m/s")
-    print(f"  test ws range:   {test['wind_speed_mps'].min():.2f} - {test['wind_speed_mps'].max():.2f} m/s")
-    print(f"  power range: {released['power_kW'].min():.2f} - {released['power_kW'].max():.2f} kW")
+    print(f"  train.csv = {len(train)} rows (calendar year 2014)")
+    print(f"  test.csv  = {len(test)} rows (calendar year 2015 + 2016 rollover)")
+    for name, d in [("train", train), ("test", test)]:
+        print(f"  {name} ws range:    {d['wind_speed_mps'].min():.2f} - {d['wind_speed_mps'].max():.2f} m/s")
+        print(f"  {name} power range: {d['power_kW'].min():.2f} - {d['power_kW'].max():.2f} kW")
     for name, p in paths.items():
         print(f"  md5 {name}={md5(p)}")
     return 0