{"id":"W2071101016","doi":"10.1007/s00024-012-0549-6","title":"A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables","year":2012,"lang":"en","type":"article","venue":"Pure and Applied Geophysics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Environment and Climate Change Canada","funders":"","keywords":"Nowcasting; Numerical weather prediction; Categorical variable; Meteorology; Weighting; Forecast skill; Wind speed; Terrain; Data assimilation; Quantitative precipitation forecast; Snow; Forecast verification; Environmental science; Climatology; Computer science; Precipitation; Geography; Machine learning; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002376792,0.0007263833,0.0009188197,0.0004868569,0.0004875582,0.001248873,0.003249925,0.0009389498,0.004719767],"category_scores_gemma":[0.009464692,0.000555146,0.0009807826,0.000701958,0.0004221321,0.001821975,0.001428369,0.001453068,0.000922076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008488444,"about_ca_system_score_gemma":0.00315458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03897548,"about_ca_topic_score_gemma":0.03287882,"domain_scores_codex":[0.998998,0.0003123951,0.00007767562,0.0002603176,0.0002414482,0.0001102603],"domain_scores_gemma":[0.9966356,0.001594934,0.0001547711,0.0005219178,0.0009585706,0.0001341851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001642207,0.0001286328,0.002927649,0.00004146195,0.00008992558,0.0000425088,0.00003869539,0.9623055,0.001250676,0.008454324,0.00100505,0.02355128],"study_design_scores_gemma":[0.000009446598,0.00000749129,0.00008596973,0.00000115595,0.000004835463,0.000002363488,0.000002061074,0.9983513,0.0001997461,0.001162864,0.0001701183,0.000002607713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063067,0.0000563675,0.8873304,0.0001542258,0.0001388081,0.00007689873,0.001487947,0.00241971,0.002028976],"genre_scores_gemma":[0.6770393,0.00005736626,0.314735,0.0001245919,0.00005321398,0.0002623436,0.003002334,0.001046668,0.003679319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03897548,"threshold_uncertainty_score":0.07749724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01894250214729648,"score_gpt":0.2089080966212905,"score_spread":0.189965594473994,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}