Upload finetuned TabPFN-SDM models (nonspatial + spatial)

.gitattributes

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+training_logs/tabpfn-sdm-spatial_training_plot.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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+
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+   ---------------------- ADDITIONAL PROVISION --------------------------
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+    10. Additional attribution.
+    If You distribute or make available the Work or any Derivative
+    Work thereof relating to any part of the source or model weights,
+    or a product or service (including another AI model) that contains
+    any source or model weights, You shall (A) provide a copy of this
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README.md

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+---
+library_name: pytorch
+pipeline_tag: tabular-classification
+base_model: Prior-Labs/TabPFN-v2-clf
+license: other
+license_name: prior-labs-license-v1.1
+license_link: LICENSE
+tags:
+  - pytorch
+  - tabpfn
+  - species-distribution-modeling
+  - ecology
+  - biodiversity
+  - tabular-classification
+  - finetuned
+metrics:
+  - roc_auc
+  - average_precision
+---
+
+# TabPFN-SDM: TabPFN Finetuned for Species Distribution Modeling
+
+**Built with PriorLabs-TabPFN**
+
+Finetuned [TabPFN](https://huggingface.co/Prior-Labs/TabPFN-v2-clf) models for binary species distribution modeling (SDM). These models were adapted from the pretrained TabPFN foundation model to improve performance on presence/absence classification tasks common in ecology and conservation biology.
+
+## Model Variants
+
+| File | Description | Epochs | Val ROC-AUC | Val PR-AUC |
+|------|-------------|--------|-------------|------------|
+| `tabpfn-sdm-nonspatial.pt` | Standard (non-spatial) train/test evaluation | 100 | 0.747 | 0.261 |
+| `tabpfn-sdm-spatial.pt` | Spatially-separated train/test evaluation | 50 | 0.653 | 0.144 |
+
+The **non-spatial** variant is trained and evaluated using standard random train/test splits. The **spatial** variant uses 10km buffer-based spatial separation between training and test data, which is more realistic for ecological applications but results in a harder prediction task.
+
+## Training Details
+
+### Two-Step Finetuning
+
+Each model was finetuned in two steps:
+
+1. **Step 1 (Cross-Validation):** Finetuned on CV-generated train/test splits from training data. Learning rate: 1e-5. Goal: adapt TabPFN to general SDM classification patterns.
+2. **Step 2 (Benchmark):** Further finetuned on actual benchmark train/test splits. Learning rate: 1e-6. Goal: refine for the specific evaluation setup.
+
+### Training Data
+
+- **226 species** across **6 geographic regions**: AWT (Australia), NSW (New South Wales), CAN (Canada), NZ (New Zealand), SA (South Africa), SWI (Switzerland)
+- Species split: 62.5% finetuning, 7.5% validation, 30% held-out test
+- Data from the `disdat` R package (standardized presence-only and presence-absence datasets)
+- Features include environmental covariates (7-12 per region) with native categorical variable support
+
+### Training Procedure
+
+- **Base model:** TabPFN v2 classifier (`Prior-Labs/TabPFN-v2-clf`)
+- **Optimizer:** AdamW
+- **LR Schedule:** OneCycleLR
+- **Training approach:** Iterative R-Python epoch loop with smart sub-batching
+  - R generates cross-validation data (parallelized with 20 cores)
+  - Python trains one epoch on GPU
+  - Smart sub-batching: keeps ALL presences (rare, valuable) in every sub-batch, partitions absences across sub-batches
+- **Max training samples per sub-batch:** 1,500
+- **Random seed:** 32639
+
+### Hardware
+
+- GPU: NVIDIA RTX 4000 ADA GPU with 20GB VRAM
+- Peak memory: ~14GB during training
+- Training time: ~188 hours total (Step 1 + Step 2) for non-spatial; ~178 hours for spatial
+
+## Usage
+
+### Python
+
+```python
+import torch
+from tabpfn import TabPFNClassifier
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="rdinnager/tabpfn-sdm-finetuned",
+    filename="tabpfn-sdm-nonspatial.pt"
+)
+
+# Load finetuned model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+clf = TabPFNClassifier(
+    ignore_pretraining_limits=True,
+    device=device,
+    n_estimators=8,
+    random_state=32639
+)
+clf._initialize_model_variables()
+
+checkpoint = torch.load(model_path, map_location=device, weights_only=False)
+clf.models_[0].load_state_dict(checkpoint["model_state_dict"])
+clf.models_[0].eval()
+
+# Fit on training data and predict
+clf.fit(X_train, y_train)
+probs = clf.predict_proba(X_test)[:, 1]  # Probability of presence
+```
+
+### R (via reticulate)
+
+The companion R code provides inference wrappers. See the [TabPFN-SDM GitHub repository](https://github.com/rdinnager/TabPFN-SDM) for the full pipeline.
+
+```r
+source("R/run_tabpfn_finetuned.R")
+
+result <- run_tabpfn_finetuned_ensemble(
+  train_dat = train_data,
+  test_dat = test_data,
+  model_path = "tabpfn-sdm-nonspatial.pt",
+  max_train_size = 1000L,
+  n_estimators = 8L
+)
+
+# result$test contains y_truth and y_pred columns
+```
+
+### Ensemble Inference (Recommended)
+
+For best results, use ensemble inference which matches the training procedure:
+- Keeps ALL presences in every sub-batch
+- Partitions absences across sub-batches for 100% data usage
+- Averages predictions across sub-batches
+
+This is implemented in `run_tabpfn_finetuned_ensemble()` (R) or can be replicated in Python using the utilities in the GitHub repo.
+
+## Checkpoint Contents
+
+Each `.pt` file is a PyTorch checkpoint dictionary containing:
+
+| Key | Description |
+|-----|-------------|
+| `model_state_dict` | Finetuned TabPFN model weights |
+| `config` | Training configuration (hyperparameters, data settings) |
+| `history` | Training history (loss, metrics per epoch) |
+| `step1_path` | Path to the Step 1 model used as initialization |
+
+## Limitations
+
+- Models were trained on species from 6 specific geographic regions and may not generalize to other regions without additional finetuning
+- Performance varies by species; species with very few presences may have lower accuracy
+- The spatial variant has lower validation metrics due to the inherently harder task of spatial extrapolation
+- Requires the `tabpfn` Python package (v2) for inference
+- Categorical variable handling requires proper setup (see project documentation)
+
+## Citation
+
+If you use these models, please cite the accompanying paper:
+
+```bibtex
+@article{dinnage2026niche,
+  title={A Niche in the Machine: The Promise of AI Foundation Models for Species Distribution Modeling},
+  author={Dinnage, Russell and Warren, Dan L.},
+  year={2026},
+  doi={10.32942/X2VQ10},
+  journal={EcoEvoRxiv},
+  url={https://ecoevorxiv.org/repository/view/11797/}
+}
+```
+
+Please also cite the original TabPFN paper:
+
+```bibtex
+@article{hollmann2025tabpfn,
+  title={Accurate Predictions on Small Data with a Tabular Foundation Model},
+  author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
+          Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
+          Schirrmeister, Robin Tibor and Hutter, Frank},
+  journal={Nature},
+  year={2025},
+  publisher={Nature Publishing Group}
+}
+```
+
+## License
+
+These finetuned model weights are distributed under the [Prior Labs License v1.1](LICENSE), consistent with the base TabPFN model license. See the LICENSE file for full terms.
+
+## Links
+
+- [TabPFN-SDM GitHub Repository](https://github.com/rdinnager/TabPFN-SDM)
+- [Base TabPFN Model](https://huggingface.co/Prior-Labs/TabPFN-v2-clf)
+- [TabPFN GitHub](https://github.com/PriorLabs/TabPFN)

config.json

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+{
+  "model_type": "tabpfn-finetuned",
+  "base_model": "Prior-Labs/TabPFN-v2-clf",
+  "task": "binary-classification",
+  "domain": "species-distribution-modeling",
+  "variants": {
+    "nonspatial": {
+      "filename": "tabpfn-sdm-nonspatial.pt",
+      "description": "Standard train/test split evaluation",
+      "step1_epochs": 40,
+      "step2_epochs": 100,
+      "final_val_roc_auc": 0.747,
+      "final_val_pr_auc": 0.261,
+      "final_train_loss": 0.460,
+      "final_val_loss": 3.429
+    },
+    "spatial": {
+      "filename": "tabpfn-sdm-spatial.pt",
+      "description": "Spatially-separated (10km buffer) evaluation",
+      "step1_epochs": 25,
+      "step2_epochs": 50,
+      "final_val_roc_auc": 0.653,
+      "final_val_pr_auc": 0.144,
+      "final_train_loss": 0.470,
+      "final_val_loss": 5.469
+    }
+  },
+  "training": {
+    "optimizer": "AdamW",
+    "lr_schedule": "OneCycleLR",
+    "step1_learning_rate": 1e-5,
+    "step2_learning_rate": 1e-6,
+    "max_train_size_per_subbatch": 1500,
+    "max_test_size": 1500,
+    "n_estimators_training": 2,
+    "n_estimators_inference": 8,
+    "seed": 32639,
+    "species_split_seed": 12345
+  },
+  "data": {
+    "total_species": 226,
+    "finetune_species_fraction": 0.625,
+    "validation_species_fraction": 0.075,
+    "test_species_fraction": 0.30,
+    "regions": ["AWT", "NSW", "CAN", "NZ", "SA", "SWI"],
+    "categorical_variables": {
+      "CAN": ["ontveg"],
+      "NSW": ["vegsys"],
+      "NZ": ["age", "toxicats"],
+      "SWI": ["calc"]
+    }
+  },
+  "checkpoint_format": {
+    "keys": ["model_state_dict", "config", "history", "step1_path"],
+    "framework": "pytorch"
+  }
+}

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training_logs/tabpfn-sdm-spatial_training_log.csv

@@ -0,0 +1,101 @@
+"epoch","train_loss","val_loss","val_auc_roc","val_pr_auc","val_cor","focal_gamma","n_batches","r_prep_time_sec","python_train_time_sec","total_epoch_time_min","timestamp"
+1,0.66133011503766,5.43197786700644,0.670745610448307,0.139653152608028,0.119366973009271,2,600,0.59018611907959,154.012180805206,3.65113788445791,"2025-12-13 01:56:28"
+2,0.648822891910871,5.48483700468314,0.673690048391566,0.142021084248995,0.123406561842589,2,600,0.524244070053101,154.167514562607,3.65423013369242,"2025-12-13 02:00:09"
+3,0.637041368794938,5.51770436180041,0.67412170333094,0.142030206333121,0.126343839253727,2,600,0.495769023895264,153.892928123474,3.64403801759084,"2025-12-13 02:03:48"
+4,0.628081634634485,5.51713122177082,0.673359618822212,0.14212787367446,0.128529743061048,2,600,0.562830924987793,156.31062078476,3.69919883410136,"2025-12-13 02:07:32"
+5,0.620072423753639,5.50895653302342,0.67190783249284,0.143172820631921,0.130110472391863,2,600,0.523153781890869,155.942580461502,3.68222611347834,"2025-12-13 02:11:14"
+6,0.620557555866738,5.47772782526128,0.671491059008382,0.143686993242808,0.131059519840359,2,600,0.540592908859253,156.581949234009,3.69372956752777,"2025-12-13 02:14:56"
+7,0.619203566809495,5.47251426197539,0.670327022940993,0.144828224981875,0.130632213606609,2,600,0.498115062713623,157.582863330841,3.70684136549632,"2025-12-13 02:18:40"
+8,0.613201983850449,5.46847387035432,0.670023492547766,0.145124494258132,0.130443602620702,2,600,0.515519857406616,156.061025381088,3.70425953070323,"2025-12-13 02:22:23"
+9,0.606503394494454,5.44025921281699,0.668898739191334,0.145053770416534,0.128364962448761,2,600,0.522512912750244,156.200025558472,3.6852768500646,"2025-12-13 02:26:05"
+10,0.606667295557757,5.44302411981601,0.667941637600183,0.144995088525137,0.127145417507384,2,600,0.514711141586304,156.025696992874,3.68507664998372,"2025-12-13 02:29:48"
+11,0.601770934058974,5.42590594976719,0.666964505201599,0.14332761633326,0.128742250131897,2,600,0.50373911857605,156.800369262695,3.69688893556595,"2025-12-13 02:33:30"
+12,0.602963122576475,5.40723882616095,0.665250151906971,0.143416388799035,0.126661364990966,2,600,0.492477893829346,156.256901264191,3.70220110019048,"2025-12-13 02:37:14"
+13,0.59463719672213,5.43815541700425,0.664453276850467,0.143055492710027,0.125518024664426,2,600,0.495738983154297,156.283204078674,3.68572201331457,"2025-12-13 02:40:56"
+14,0.596838751770556,5.44414110832634,0.664046166505816,0.142455565557586,0.124868984556363,2,600,0.529523849487305,157.180052995682,3.70262074867884,"2025-12-13 02:44:39"
+15,0.592944882214069,5.44766626299487,0.661732804036588,0.142896506213931,0.122777388630609,2,600,0.507354974746704,156.884328603745,3.69587616920471,"2025-12-13 02:48:22"
+16,0.584197715645035,5.43586105904569,0.660802060377689,0.14218491467943,0.121077319728556,2,600,0.492301940917969,156.372521400452,3.6951115489006,"2025-12-13 02:52:05"
+17,0.586351085181038,5.43198340960205,0.658959712816418,0.140314676974842,0.119500222809772,2,600,0.482590913772583,156.139464139938,3.68682479858398,"2025-12-13 02:55:47"
+18,0.583555960953236,5.4032829828369,0.660010144191298,0.140655056118704,0.118579857455798,2,600,0.513795852661133,157.251111030579,3.70589254697164,"2025-12-13 02:59:30"
+19,0.574378057848662,5.40787919876909,0.660856557462936,0.142989621179426,0.116897738950912,2,600,0.493882179260254,157.078800439835,3.70063393513362,"2025-12-13 03:03:14"
+20,0.570146950818598,5.47463863037854,0.656560015498946,0.140894558871864,0.118733277016238,2,600,0.505942106246948,156.026789665222,3.68267560005188,"2025-12-13 03:06:56"
+21,0.566125295658906,5.40485122784063,0.658316035562614,0.140191350283117,0.114558582564849,2,600,0.493243932723999,157.39087677002,3.70931661526362,"2025-12-13 03:10:39"
+22,0.564854275497297,5.40888369056514,0.657337443097458,0.138962811090124,0.114141964780073,2,600,0.516684055328369,157.582018375397,3.70761251846949,"2025-12-13 03:14:23"
+23,0.560872377784302,5.44691908786361,0.654942585527083,0.138564496059931,0.113185469547371,2,600,0.499988079071045,157.798291444778,3.71325266758601,"2025-12-13 03:18:07"
+24,0.560609861711661,5.45712277382913,0.656483146302856,0.137815351980198,0.109509869605279,2,600,0.503304958343506,156.611301422119,3.6940709511439,"2025-12-13 03:21:49"
+25,0.556475588710358,5.5102384226365,0.656020668163563,0.139160175437283,0.110659916927053,2,600,0.498593807220459,156.111125707626,3.68619733254115,"2025-12-13 03:25:32"
+26,0.550561249864598,5.45321441539419,0.657442173057463,0.138534760903372,0.111086264341503,2,600,0.513186931610107,157.190064430237,3.70293483336767,"2025-12-13 03:29:15"
+27,0.547843172339102,5.50559636309354,0.657539399201775,0.134392383148627,0.104425983672748,2,600,0.507617950439453,156.529401063919,3.71110899845759,"2025-12-13 03:32:59"
+28,0.545411957204342,5.48809357099035,0.659134121465411,0.137974915451302,0.108987667914184,2,600,0.489403963088989,156.075370550156,3.68631529808044,"2025-12-13 03:36:41"
+29,0.543104917829235,5.44178022755263,0.658637950812583,0.139465990060233,0.110350640743571,2,600,0.534718990325928,157.512508630753,3.71154086589813,"2025-12-13 03:40:25"
+30,0.537606932359437,5.4951791481269,0.655987110414495,0.137650961648043,0.106285113890833,2,600,0.478991031646729,157.366530179977,3.70387683312098,"2025-12-13 03:44:08"
+31,0.5371891899469,5.49234772427144,0.658799009423439,0.139025514514834,0.106095013607991,2,600,0.504710912704468,156.358971357346,3.69996521870295,"2025-12-13 03:47:52"
+32,0.531644212858131,5.4916220887328,0.660092497330476,0.139729706314392,0.110478480501836,2,600,0.487482786178589,156.745415449142,3.69405553340912,"2025-12-13 03:51:34"
+33,0.535221464534601,5.54774567454018,0.65372824516165,0.136033613879294,0.108624855034052,2,600,0.495734930038452,157.725230693817,3.71225751638412,"2025-12-13 03:55:18"
+34,0.530982256674518,5.63552286329925,0.655244908787125,0.143074195985396,0.105320852065119,2,600,0.498605012893677,158.276722669601,3.72004066705704,"2025-12-13 03:59:02"
+35,0.525531236045063,5.52095707279072,0.657491510053064,0.140345936915565,0.10446397252887,2,600,0.504189968109131,157.913911342621,3.7224933664004,"2025-12-13 04:02:47"
+36,0.529042201675475,5.51886706447101,0.658597833432809,0.138463594738983,0.106844259424934,2,600,0.516534090042114,156.342707395554,3.68975656827291,"2025-12-13 04:06:29"
+37,0.525630223847305,5.36506497373415,0.658536467628414,0.140203155999235,0.100876386231405,2,600,0.521502017974854,157.498282432556,3.70863480170568,"2025-12-13 04:10:13"
+38,0.519716916702067,5.5086159815161,0.659014529279065,0.139915215625088,0.101619957244396,2,600,0.508012056350708,157.344186306,3.70586160024007,"2025-12-13 04:13:56"
+39,0.513466083373254,5.48253108174259,0.660361109782894,0.141398106446374,0.103276408803117,2,600,0.494580984115601,155.781613111496,3.6984872341156,"2025-12-13 04:17:39"
+40,0.517816905233388,5.45498836513626,0.656196773781999,0.138736489762692,0.103918677187455,2,600,0.50292706489563,156.170919895172,3.68679181734721,"2025-12-13 04:21:22"
+41,0.518740259129554,5.3903934150448,0.659446635440367,0.140108858912503,0.10458510369454,2,600,0.516641139984131,157.835290670395,3.71502739985784,"2025-12-13 04:25:06"
+42,0.513920828206465,5.40938836159856,0.660665408176331,0.138511340596386,0.0984599091908225,2,600,0.499431133270264,157.980466365814,3.71510881582896,"2025-12-13 04:28:50"
+43,0.508603686150163,5.53116702751797,0.658361876332115,0.142049579041706,0.102729573041361,2,600,0.507328987121582,156.763083934784,3.70455094973246,"2025-12-13 04:32:33"
+44,0.507055316474289,5.38513635272469,0.664642702958144,0.142470212314554,0.105892574377815,2,600,0.490265846252441,156.693947553635,3.69824121395747,"2025-12-13 04:36:16"
+45,0.506592035017287,5.49175521412687,0.661178308072416,0.14276769770409,0.101985045334578,2,600,0.501605987548828,158.408622980118,3.72330698172251,"2025-12-13 04:40:01"
+46,0.50628116668202,5.52929704768306,0.650791207107069,0.139648265404471,0.100578580611392,2,600,0.506125926971436,157.881425380707,3.71380508343379,"2025-12-13 04:43:45"
+47,0.502222297688325,5.55231755078184,0.657564445876355,0.140291883061473,0.0979291951681056,2,600,0.503492116928101,156.413066864014,3.68925034999847,"2025-12-13 04:47:27"
+48,0.503654527623827,5.47871458717328,0.656646326830194,0.141148867703291,0.100237759445202,2,600,0.515244960784912,157.101129770279,3.70066798528035,"2025-12-13 04:51:10"
+49,0.501515589772413,5.40637013759987,0.656603511159111,0.138990378002705,0.100901062992537,2,600,0.513527154922485,158.187258481979,3.71738744974136,"2025-12-13 04:54:54"
+50,0.497407663296908,5.44465786191118,0.661399368914047,0.143988881029422,0.101958156385721,2,600,0.50278902053833,157.112701416016,3.70234641631444,"2025-12-13 04:58:38"
+51,0.49754767392762,5.42578225592988,0.657699321546058,0.137707683098454,0.10321195063831,2,600,0.492694854736328,157.207679033279,3.7047663807869,"2025-12-13 05:02:21"
+52,0.498897432899103,5.4724515669034,0.653708494337314,0.137722014952683,0.0959166734609853,2,600,0.487442016601562,152.777816057205,3.61337099870046,"2025-12-13 05:05:59"
+53,0.495304858793194,5.41205035775821,0.658384026753435,0.142593089750602,0.0965027877732891,2,600,0.480426073074341,152.960811853409,3.61255061626434,"2025-12-13 05:09:38"
+54,0.49674395384267,5.4087408483338,0.656135371176494,0.141402634716936,0.0927304253074873,2,600,0.473854064941406,152.637813091278,3.61672813495,"2025-12-13 05:13:16"
+55,0.494859989536926,5.33761225865395,0.657686462872466,0.141715158959485,0.096101137651406,2,600,0.481580972671509,151.572769641876,3.59472594658534,"2025-12-13 05:16:53"
+56,0.492687129213785,5.42957134605358,0.659063454013234,0.140080247504346,0.0976257586510514,2,600,0.476223945617676,152.621817111969,3.60775901873906,"2025-12-13 05:20:31"
+57,0.491601563319564,5.46484153268307,0.659571338726311,0.142396550436263,0.0995766751458575,2,600,0.486103057861328,152.32784485817,3.60749226411184,"2025-12-13 05:24:09"
+58,0.492928552692756,5.30888979540547,0.660438209605518,0.139417887192402,0.100359012655833,2,600,0.500267028808594,150.721856355667,3.59661498069763,"2025-12-13 05:27:46"
+59,0.484412269176294,5.47605652079411,0.652318014786923,0.13785316019974,0.0953922856420005,2,600,0.532385110855103,152.263721704483,3.60823798576991,"2025-12-13 05:31:24"
+60,0.489072405782839,5.43044947926784,0.654703276141915,0.137362924743415,0.0980911735846423,2,600,0.504750967025757,153.484519481659,3.62384406725566,"2025-12-13 05:35:03"
+61,0.486353518902324,5.43200050300393,0.654221490013741,0.141204066271307,0.0976429732124004,2,600,0.492192983627319,151.32664513588,3.59392528533936,"2025-12-13 05:38:40"
+62,0.486339424241645,5.47892737982132,0.656222996091742,0.14172565592903,0.0979328798812485,2,600,0.482625007629395,151.704662799835,3.59565803209941,"2025-12-13 05:42:17"
+63,0.487735315083216,5.47456845804295,0.65746589094739,0.141222912013246,0.100978032154184,2,600,0.473495006561279,150.69663977623,3.58244829972585,"2025-12-13 05:45:53"
+64,0.486357562000242,5.47460227115136,0.653462105775844,0.139834854463075,0.0959312292760745,2,600,0.50140905380249,152.151740312576,3.59986780087153,"2025-12-13 05:49:31"
+65,0.48154096238936,5.41775244971073,0.651903336458823,0.142425643356855,0.0983947071705921,2,600,0.516759872436523,151.467621326447,3.61068049669266,"2025-12-13 05:53:09"
+66,0.483554791333154,5.49972801911735,0.65261919233216,0.139394198966061,0.0934307662873996,2,600,0.501183986663818,151.625838041306,3.59279083013535,"2025-12-13 05:56:46"
+67,0.480414719063168,5.5424667794416,0.650166095593046,0.139076269674139,0.0943678153544261,2,600,0.495907068252563,151.623754501343,3.59529131650925,"2025-12-13 06:00:23"
+68,0.481778728424882,5.52818347572783,0.653343944589792,0.140718323146849,0.0942583563333215,2,600,0.497418165206909,151.604543209076,3.5971097668012,"2025-12-13 06:04:00"
+69,0.478732469504078,5.50152213517391,0.654070112959593,0.141458015924662,0.0957264519128124,2,600,0.486480951309204,151.259647369385,3.5908268849055,"2025-12-13 06:07:37"
+70,0.481897206058105,5.49433281842778,0.654824154820738,0.142231114478726,0.0996254776009345,2,600,0.494326114654541,151.575616359711,3.59315555095673,"2025-12-13 06:11:14"
+71,0.479115151216586,5.52685291984992,0.654179136111562,0.139145567782715,0.0944531893830701,2,600,0.508790016174316,152.569899559021,3.61246101458867,"2025-12-13 06:14:52"
+72,0.478682938686882,5.53518756068611,0.654115631783108,0.139930542501791,0.0971041731461897,2,600,0.487629890441895,151.230334043503,3.59286016623179,"2025-12-13 06:18:29"
+73,0.471463195784017,5.49309100306665,0.654924747940641,0.140774605690035,0.0942635046306534,2,600,0.497917890548706,151.572294235229,3.59580980141958,"2025-12-13 06:22:06"
+74,0.479630691539496,5.53563929663218,0.654084084582525,0.140642018008212,0.0942642547421547,2,600,0.491114854812622,151.610561609268,3.59238001505534,"2025-12-13 06:25:43"
+75,0.474328287538762,5.51505848919406,0.654941724975066,0.140974605960947,0.097092377415075,2,600,0.49444580078125,152.238273620605,3.60468896627426,"2025-12-13 06:29:21"
+76,0.478522763488193,5.45531566969372,0.653768509791882,0.14134835958622,0.0951731332549127,2,600,0.531599044799805,152.182258605957,3.60261193116506,"2025-12-13 06:32:58"
+77,0.47531782226637,5.50344132568036,0.653572397634421,0.142549161433778,0.0943440180178006,2,600,0.490708112716675,152.517019510269,3.6089008529981,"2025-12-13 06:36:36"
+78,0.479323625164106,5.41824103187086,0.654387092149465,0.141513689384534,0.0954889076627389,2,600,0.490542888641357,153.879976034164,3.6311515490214,"2025-12-13 06:40:16"
+79,0.474868357454737,5.47943336367112,0.655600631216014,0.142785874254546,0.0934664045904071,2,600,0.502581834793091,152.169291257858,3.60855063199997,"2025-12-13 06:43:54"
+80,0.470489296568558,5.52708409539493,0.653310393746797,0.142310658502511,0.0930018113649282,2,600,0.49401593208313,151.436303138733,3.5923304160436,"2025-12-13 06:47:31"
+81,0.469071060256101,5.48144353975817,0.657144837413458,0.145281388301393,0.0965775381790437,2,600,0.516509056091309,151.811748743057,3.59656045039495,"2025-12-13 06:51:08"
+82,0.47797984091565,5.52392360277687,0.655774224256036,0.141971082624762,0.0961160643141496,2,600,0.505538940429688,152.749043703079,3.6112105846405,"2025-12-13 06:54:46"
+83,0.471493759794782,5.50271597741301,0.653042194206227,0.140544094240847,0.0923955410668171,2,600,0.473028898239136,151.791126966476,3.60136553049088,"2025-12-13 06:58:23"
+84,0.471767753356447,5.44343611787521,0.652215486612565,0.139930394894934,0.0923909712430609,2,600,0.501204013824463,151.04732966423,3.58508960008621,"2025-12-13 07:02:00"
+85,0.47179202428398,5.46103367689546,0.653163210720166,0.140848904477437,0.0922080499683235,2,600,0.486063957214355,152.141865968704,3.60137846469879,"2025-12-13 07:05:37"
+86,0.47063261874641,5.45657373040291,0.652604631540288,0.140618577991799,0.0909138892945543,2,600,0.508774042129517,152.026038646698,3.6014527519544,"2025-12-13 07:09:15"
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