WeatherGPT model bundle

.gitattributes

@@ -33,3 +33,4 @@ 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
+m1_field_mapper_v1/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,146 @@
+---
+license: apache-2.0
+library_name: weathergpt-models
+tags:
+  - weather
+  - post-processing
+  - india
+  - meteorology
+---
+
+# WeatherGPT model bundle
+
+Five models for the WeatherGPT meteorological interoperability layer.
+Every number below was produced by a training script and copied from a
+`metrics.json`; none was typed by hand.
+
+```python
+from weathergpt_models import ModelRegistry
+registry = ModelRegistry.from_hub("Arko007/weathergpt-models")
+print(registry.status())
+```
+
+Each model is gated on beating the baseline it replaces. An artifact
+that cannot prove its provenance, or that does not beat its baseline,
+is refused at load time and the caller falls back to a deterministic
+path.
+
+## field_mapper — `m1_field_mapper_v1`
+
+**label-embedding bi-encoder + multitask heads**
+
+- dataset: `d3_authoritative_parameter_tables`
+- dataset sha256: `e5d3562cedf16d653f75d84adf51868d`
+- split: by source table — train: CF+GRIB2+CAP; dev_zeroshot: BUFR+OpenMeteo+IMD (threshold calibration only); test_zeroshot: WRF+NCEP (never touched until the final measurement)
+- trained: 2026-09-03T16:33:08.084847Z
+- admission gate: **PASS** — zero-shot macro-F1 0.743 vs dict registry 0.142
+
+| metric | model | dict registry | majority |
+|---|---|---|---|
+| zero-shot macro-F1 | **0.7432** | 0.1416 | 0.0217 |
+| zero-shot accuracy | **0.8319** | 0.4907 | 0.4356 |
+| statistic accuracy | 0.9792 | — | — |
+| misassignment rate | 0.0016 | — | — |
+
+The test set is parameter tables the model never trained on (WRF Registry, NCEP GFS inventories, the WMO BUFR element table, Open-Meteo and IMD product fields), so this measures generalisation to a schema nobody has mapped.
+
+## mos — `m2_mos_v1`
+
+**monotone quantile network (pinball) + LightGBM quantile forest, blended at a validation-chosen weight, with conformalised intervals**
+
+- dataset: `d1_multi_model_nwp_vs_era5_seamless`
+- dataset sha256: `a3888e44c7a6bd09b8282f214b68b1e6`
+- split: chronological 70% cutoff AND 20% spatially held-out locations
+- trained: 2026-09-04T06:29:10.987471Z
+- admission gate: **PASS** — positive CRPS skill against the raw ensemble on the spatial holdout for every variable
+
+| variable | CRPS | raw ensemble CRPS | CRPS skill |
+|---|---|---|---|
+| temperature_2m | 0.6162 | 0.6469 | **0.047** |
+| precipitation | 0.2190 | 0.2523 | **0.132** |
+| wind_speed_10m | 1.6000 | 1.7570 | **0.089** |
+
+## intent — `m3_intent_v1`
+
+**JointBERT: intent + BIO slots + multi-label variables on a shared encoder**
+
+- dataset: `d4_multilingual_templated_queries_with_exact_slot_spans`
+- dataset sha256: `cb2ee6c4e1d8d9857975d3362957d90c`
+- split: held-out template families {sow,heat,storm} AND 20% held-out districts
+- trained: 2026-09-04T08:43:34.868234Z
+- admission gate: **PASS** — intent macro-F1 0.746 vs rule parser 0.166, slot F1 0.988
+
+| metric | model | rule parser |
+|---|---|---|
+| intent macro-F1 | **0.7459** | 0.1664 |
+| intent accuracy | **0.8361** | 0.5129 |
+| slot F1 (seqeval) | 0.9878 | not supported |
+| variable micro-F1 | 0.1704 | not supported |
+
+Held out: whole template families and whole districts, so neither a memorised sentence pattern nor a memorised place name can inflate this.
+
+| language | n | intent macro-F1 | slot F1 |
+|---|---|---|---|
+| as | 91 | 0.7046 | 0.9819 |
+| bn | 91 | 0.7486 | 0.9922 |
+| bn_latn | 91 | 0.7774 | 0.9688 |
+| en | 94 | 0.7477 | 0.9824 |
+| gu | 91 | 0.7242 | 0.9948 |
+| hi | 91 | 0.7475 | 0.9870 |
+| hi_latn | 91 | 0.7508 | 0.9691 |
+| kn | 91 | 0.7679 | 0.9948 |
+| ml | 89 | 0.7538 | 1.0000 |
+| mr | 91 | 0.7475 | 0.9948 |
+| or | 91 | 0.7644 | 0.9923 |
+| pa | 91 | 0.7475 | 0.9819 |
+| ta | 91 | 0.7750 | 0.9948 |
+| te | 91 | 0.7475 | 0.9948 |
+
+## calibration — `m4_calibration_v1`
+
+**hurdle CSGD (precipitation: P(wet) x censored shifted gamma) + Gaussian EMOS (temperature, wind), CRPS-fitted, with isotonic-refined exceedance probabilities**
+
+- dataset: `d1_multi_model_nwp_vs_era5_seamless`
+- dataset sha256: `a3888e44c7a6bd09b8282f214b68b1e6`
+- split: chronological 70% cutoff AND 20% spatially held-out locations
+- trained: 2026-09-04T06:00:18.494934Z
+- admission gate: **PASS** — calibrated Brier beats raw ensemble frequency at every threshold
+
+| variable | CRPS | raw ensemble CRPS | CRPS skill |
+|---|---|---|---|
+| precipitation | 0.2491 | 0.2315 | **-0.076** |
+
+Precipitation exceedance, Brier score (lower is better):
+
+| threshold | base rate | raw member count | calibrated | climatology |
+|---|---|---|---|---|
+| >0.1 mm | 0.3161 | 0.16660 | **0.14883** | 0.21616 |
+| >1.0 mm | 0.0932 | 0.08376 | **0.07243** | 0.08455 |
+| >5.0 mm | 0.0083 | 0.00909 | **0.00807** | 0.00822 |
+| >10.0 mm | 0.0012 | 0.00155 | **0.00124** | 0.00124 |
+| >25.0 mm | 0.0001 | 0.00014 | **0.00010** | 0.00010 |
+| >50.0 mm | 0.0000 | 0.00000 | **0.00000** | 0.00000 |
+
+> **Transfer assumption.** Trained on a 4-member multi-model ensemble; served against the 31-member GFS ensemble. Only summary statistics cross the boundary, so the interface is identical, but the spread mapping is assumed rather than measured: the ensemble API serves members only for ~the last 4 days while ERA5 truth lags ~6, so the two windows never overlap and the transfer could not be verified.
+
+## trust_ranker — `m5_trust_ranker_v1`
+
+**LightGBM LambdaMART over candidate NWP sources per (location, valid time, lead) group**
+
+- dataset: `d1_multi_model_nwp_vs_era5_seamless`
+- dataset sha256: `99fd37b00135687f2c1085d0e32fb99c`
+- split: chronological 70% cutoff AND 20% spatially held-out locations
+- trained: 2026-09-03T16:25:33.240955Z
+- admission gate: **PASS** — beats the fixed authority order on 3/3 variables
+
+| variable | NDCG@1 | picks the best source | fixed authority does | RMSE following ranker | RMSE following fixed order |
+|---|---|---|---|---|---|
+| temperature_2m | 0.6876 | **0.383** | 0.346 | **1.308** | 1.443 |
+| precipitation | 0.8506 | **0.237** | 0.170 | **1.056** | 1.124 |
+| wind_speed_10m | 0.6777 | **0.377** | 0.317 | **3.993** | 4.582 |
+
+## Provenance
+
+Bundle built 2026-09-04T09:04:25.981803Z from the `weathergpt-models` Modal volume.
+
+Training corpora were built from Open-Meteo (multi-model NWP archives and ERA5 reanalysis), the CF standard name table, the ECMWF eccodes GRIB2 definitions, NOAA NCEP GRIB2 code tables and GFS inventories, the WRF Registry, the WMO BUFR element table, and the live SACHET/NDMA CAP feed.

bundle.json

@@ -0,0 +1,36 @@
+{
+  "built_at": "2026-09-04T09:04:25.982012Z",
+  "models": {
+    "field_mapper": {
+      "algorithm_version": "m1_field_mapper_v1",
+      "gate_ok": true,
+      "gate_reason": "zero-shot macro-F1 0.743 vs dict registry 0.142",
+      "dataset_sha256": "e5d3562cedf16d653f75d84adf51868d7082f2372f1e4e7a7caad44925925547"
+    },
+    "mos": {
+      "algorithm_version": "m2_mos_v1",
+      "gate_ok": true,
+      "gate_reason": "positive CRPS skill against the raw ensemble on the spatial holdout for every variable",
+      "dataset_sha256": "a3888e44c7a6bd09b8282f214b68b1e649ca17b3250cfcff7e6fac97645ad76a"
+    },
+    "intent": {
+      "algorithm_version": "m3_intent_v1",
+      "gate_ok": true,
+      "gate_reason": "intent macro-F1 0.746 vs rule parser 0.166, slot F1 0.988",
+      "dataset_sha256": "cb2ee6c4e1d8d9857975d3362957d90cf6ba8f8e242a4441af264c17645207fc"
+    },
+    "calibration": {
+      "algorithm_version": "m4_calibration_v1",
+      "gate_ok": true,
+      "gate_reason": "calibrated Brier beats raw ensemble frequency at every threshold",
+      "dataset_sha256": "a3888e44c7a6bd09b8282f214b68b1e649ca17b3250cfcff7e6fac97645ad76a"
+    },
+    "trust_ranker": {
+      "algorithm_version": "m5_trust_ranker_v1",
+      "gate_ok": true,
+      "gate_reason": "beats the fixed authority order on 3/3 variables",
+      "dataset_sha256": "99fd37b00135687f2c1085d0e32fb99cf67a0928114364ff7b5a6e9e43cf2339"
+    }
+  },
+  "skipped": []
+}

m1_field_mapper_v1/config.json

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+{
+  "base_model": "intfloat/multilingual-e5-base",
+  "max_len": 64,
+  "abstention_threshold": 0.5735692214965821,
+  "canonical_variables": [
+    "precipitation_probability",
+    "precipitation_rate",
+    "precipitation_amount",
+    "snowfall_amount",
+    "snow_depth",
+    "temperature_max",
+    "temperature_min",
+    "dewpoint_2m",
+    "apparent_temperature",
+    "soil_temperature",
+    "sea_surface_temperature",
+    "temperature_2m",
+    "wind_gust",
+    "wind_u",
+    "wind_v",
+    "wind_direction",
+    "wind_speed",
+    "humidity",
+    "specific_humidity",
+    "pressure_msl",
+    "pressure_surface",
+    "solar_radiation",
+    "sunshine_duration",
+    "cloud_cover",
+    "visibility",
+    "soil_moisture",
+    "evapotranspiration",
+    "cape",
+    "cin",
+    "lightning_density",
+    "thunderstorm_probability",
+    "wave_height",
+    "wave_period",
+    "heavy_rain_warning",
+    "thunderstorm_warning",
+    "cyclone_warning",
+    "heat_warning",
+    "cold_wave_warning",
+    "fog_warning",
+    "hail_warning",
+    "flood_warning",
+    "marine_warning",
+    "dust_storm_warning",
+    "snow_warning",
+    "rainfall_distribution",
+    "other"
+  ],
+  "statistics": [
+    "instant",
+    "accumulation",
+    "mean",
+    "max",
+    "min",
+    "probability",
+    "categorical"
+  ],
+  "levels": [
+    "surface",
+    "2m",
+    "10m",
+    "mean_sea_level",
+    "pressure_level",
+    "soil",
+    "column",
+    "sea_surface",
+    "tropopause",
+    "height_above_ground",
+    "cloud_base",
+    "cloud_top",
+    "boundary_layer",
+    "other"
+  ],
+  "evidence_classes": [
+    "forecast",
+    "observation",
+    "reanalysis",
+    "nowcast",
+    "warning",
+    "climatology",
+    "advisory",
+    "radar",
+    "satellite"
+  ]
+}

m1_field_mapper_v1/label_embeddings.npy

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m1_field_mapper_v1/metrics.json

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+{
+  "algorithm_version": "m1_field_mapper_v1",
+  "model_kind": "label-embedding bi-encoder + multitask heads",
+  "base_model": "intfloat/multilingual-e5-base",
+  "dataset_kind": "d3_authoritative_parameter_tables",
+  "dataset_path": "/data/d3_fields.parquet",
+  "dataset_sha256": "e5d3562cedf16d653f75d84adf51868d7082f2372f1e4e7a7caad44925925547",
+  "split": "by source table \u2014 train: CF+GRIB2+CAP; dev_zeroshot: BUFR+OpenMeteo+IMD (threshold calibration only); test_zeroshot: WRF+NCEP (never touched until the final measurement)",
+  "n_train": 7286,
+  "n_val": 991,
+  "n_dev_zeroshot": 1829,
+  "n_test": 5140,
+  "epochs": 10,
+  "batch_size": 64,
+  "lr": 2e-05,
+  "seed": 42,
+  "abstention_threshold": 0.5735692214965821,
+  "abstention_threshold_calibrated_on": "dev_zeroshot (held-out schemas, disjoint from the reported test set)",
+  "trained_at": "2026-09-03T16:33:08.084847Z",
+  "val_accuracy": 0.9616548940464178,
+  "val_macro_f1": 0.8429684669968456,
+  "val_mapped_accuracy": 0.9017341040462428,
+  "val_statistic_accuracy": 0.9334006054490414,
+  "val_level_accuracy": 0.9667003027245207,
+  "val_evidence_class_accuracy": 0.9969727547931383,
+  "val_n": 991,
+  "val_abstain_precision": 0.9851668726823238,
+  "val_abstain_recall": 0.9743276283618582,
+  "val_misassignment_rate": 0.005045408678102927,
+  "val_hallucination_rate": 0.02119071644803229,
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+  "dev_zeroshot_macro_f1": 0.7483785685720807,
+  "dev_zeroshot_mapped_accuracy": 0.6893617021276596,
+  "dev_zeroshot_statistic_accuracy": 0.963914707490432,
+  "dev_zeroshot_level_accuracy": 0.9732094040459267,
+  "dev_zeroshot_evidence_class_accuracy": 0.9852378348824494,
+  "dev_zeroshot_n": 1829,
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+  "dev_zeroshot_abstain_recall": 0.9661229611041405,
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+  "dev_zeroshot_hallucination_rate": 0.02952433023510115,
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+  "test_zeroshot_mapped_accuracy": 0.7435367114788004,
+  "test_zeroshot_statistic_accuracy": 0.9791828793774319,
+  "test_zeroshot_level_accuracy": 0.5673151750972762,
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+  "test_zeroshot_n": 5140,
+  "test_zeroshot_abstain_precision": 0.7422066549912434,
+  "test_zeroshot_abstain_recall": 0.9464046449307727,
+  "test_zeroshot_misassignment_rate": 0.0015564202334630351,
+  "test_zeroshot_hallucination_rate": 0.023346303501945526,
+  "baselines": {
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+      "mapped_accuracy": 0.09755256807997242,
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+      "mapped_coverage": 0.011560693641618497
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+    "majority_class_zeroshot": {
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+      "macro_f1": 0.02167347491917217
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+  "zeroshot_by_source": {
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+      "accuracy": 0.8132578819725141,
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+      "mapped_accuracy": 0.752745172283226
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+      "macro_f1": 0.4838467510054547,
+      "mapped_accuracy": 0.65
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+  },
+  "label_space": [
+    "precipitation_probability",
+    "precipitation_rate",
+    "precipitation_amount",
+    "snowfall_amount",
+    "snow_depth",
+    "temperature_max",
+    "temperature_min",
+    "dewpoint_2m",
+    "apparent_temperature",
+    "soil_temperature",
+    "sea_surface_temperature",
+    "temperature_2m",
+    "wind_gust",
+    "wind_u",
+    "wind_v",
+    "wind_direction",
+    "wind_speed",
+    "humidity",
+    "specific_humidity",
+    "pressure_msl",
+    "pressure_surface",
+    "solar_radiation",
+    "sunshine_duration",
+    "cloud_cover",
+    "visibility",
+    "soil_moisture",
+    "evapotranspiration",
+    "cape",
+    "cin",
+    "lightning_density",
+    "thunderstorm_probability",
+    "wave_height",
+    "wave_period",
+    "heavy_rain_warning",
+    "thunderstorm_warning",
+    "cyclone_warning",
+    "heat_warning",
+    "cold_wave_warning",
+    "fog_warning",
+    "hail_warning",
+    "flood_warning",
+    "marine_warning",
+    "dust_storm_warning",
+    "snow_warning",
+    "rainfall_distribution",
+    "other"
+  ]
+}

m1_field_mapper_v1/model.pt

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m1_field_mapper_v1/special_tokens_map.json

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m1_field_mapper_v1/tokenizer_config.json

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+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

m2_mos_v1/metrics.json

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+  "algorithm_version": "m2_mos_v1",
+  "model_kind": "monotone quantile network (pinball) + LightGBM quantile forest, blended at a validation-chosen weight, with conformalised intervals",
+  "dataset_kind": "d1_multi_model_nwp_vs_era5_seamless",
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+  "dataset_sha256": "a3888e44c7a6bd09b8282f214b68b1e649ca17b3250cfcff7e6fac97645ad76a",
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+  "split_cutoff": "2026-05-03 02:00:00+00:00",
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+    0.25,
+    0.5,
+    0.75,
+    0.9,
+    0.95
+  ],
+  "epochs": 30,
+  "lr": 0.002,
+  "seed": 42,
+  "target_transforms": {
+    "precipitation": "cbrt"
+  },
+  "models_in_ensemble": [
+    "gfs_seamless",
+    "ecmwf_ifs025",
+    "icon_seamless",
+    "gem_seamless"
+  ],
+  "contracts": {
+    "dataset": "d1_mos",
+    "passed": true,
+    "n_checks": 17,
+    "failed": [],
+    "checks": [
+      {
+        "check": "non_empty",
+        "ok": true,
+        "detail": "9582912 rows < 200000",
+        "fatal": true
+      },
+      {
+        "check": "truth_present::truth_temperature_2m",
+        "ok": true,
+        "detail": "only 100.0% of truth_temperature_2m is finite \u2014 a truth source returning nulls silently produces an untrainable target",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_temperature_2m_gfs_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 2.41e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_temperature_2m_ecmwf_ifs025",
+        "ok": true,
+        "detail": "max |forecast - truth| = 2.14e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_temperature_2m_icon_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 2.09e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_temperature_2m_gem_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 1.74e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "truth_present::truth_precipitation",
+        "ok": true,
+        "detail": "only 100.0% of truth_precipitation is finite \u2014 a truth source returning nulls silently produces an untrainable target",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_precipitation_gfs_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 9.08e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_precipitation_ecmwf_ifs025",
+        "ok": true,
+        "detail": "max |forecast - truth| = 8.78e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_precipitation_icon_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 7.96e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_precipitation_gem_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 6.13e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "truth_present::truth_wind_speed_10m",
+        "ok": true,
+        "detail": "only 100.0% of truth_wind_speed_10m is finite \u2014 a truth source returning nulls silently produces an untrainable target",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_wind_speed_10m_gfs_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 8.06e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_wind_speed_10m_ecmwf_ifs025",
+        "ok": true,
+        "detail": "max |forecast - truth| = 4.54e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_wind_speed_10m_icon_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 4.59e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "non_degenerate::fc_wind_speed_10m_gem_seamless",
+        "ok": true,
+        "detail": "max |forecast - truth| = 5.55e+01; the two columns are the same data, so the learning target is identically zero",
+        "fatal": true
+      },
+      {
+        "check": "lead_error_grows_with_lead",
+        "ok": true,
+        "detail": "mean |error| slope vs lead = 0.069757 per unit lead; a non-positive slope means the lead column carries no forecast-age information. profile={0.0: 1.624, 1.0: 1.792, 2.0: 1.878, 3.0: 1.94, 4.0: 1.998, 5.0: 2.053, 6.0: 2.107, 7.0: 2.153}",
+        "fatal": false
+      }
+    ]
+  },
+  "trained_at": "2026-09-04T06:29:10.987471Z",
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m2_mos_v1/quantile_nets.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01989e499852366424b045e05a4cc9f72e675bbc2fc6c3b88d6868f47d78fdb5
+size 1320026

m3_intent_v1/config.json

@@ -0,0 +1,71 @@
+{
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+  "max_len": 64,
+  "intents": [
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+    "spray",
+    "irrigate",
+    "harvest",
+    "marine",
+    "travel",
+    "sow",
+    "warning_check"
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+    "B-LOC",
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+    "sunshine_duration",
+    "cloud_cover",
+    "visibility",
+    "soil_moisture",
+    "evapotranspiration",
+    "cape",
+    "cin",
+    "lightning_density",
+    "thunderstorm_probability",
+    "wave_height",
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+    "snow_warning",
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+    "other"
+  ]
+}

m3_intent_v1/metrics.json

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+    }
+  }
+}

m5_trust_ranker_v1/metrics.json

@@ -0,0 +1,363 @@
+{
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+  "model_kind": "LightGBM LambdaMART over candidate NWP sources per (location, valid time, lead) group",
+  "dataset_kind": "d1_multi_model_nwp_vs_era5_seamless",
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+  "dataset_sha256": "99fd37b00135687f2c1085d0e32fb99cf67a0928114364ff7b5a6e9e43cf2339",
+  "split": "chronological 70% cutoff AND 20% spatially held-out locations",
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+  "held_out_locations": [
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+    "1254163",
+    "1255364",
+    "1256523",
+    "1262330",
+    "1263220",
+    "1265491",
+    "1266285",
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+    "1269507",
+    "1269843",
+    "1270583",
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+    "1273467",
+    "1273780",
+    "1275339",
+    "1275665",
+    "1276533",
+    "1277029",
+    "1278710",
+    "1278718",
+    "1279259",
+    "1279290",
+    "8531979"
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