{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001001055,0.00008633171,0.0001530219,0.0000348289,0.00004106747,0.00001118138,0.00006796392,0.0000655903,0.0001847165],"category_scores_gemma":[0.000007545771,0.00006386171,0.00001718253,0.0002640666,0.00002796333,0.00009737156,0.000005106637,0.00009882576,0.00002698396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002274073,"about_ca_system_score_gemma":0.00002768779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003464606,"about_ca_topic_score_gemma":0.00003699085,"domain_scores_codex":[0.9994233,0.0000148389,0.0001599863,0.0001228883,0.00009413465,0.0001848332],"domain_scores_gemma":[0.9996522,0.0000900285,0.00004780684,0.00009304659,0.00001295261,0.000103985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002521239,0.0003075395,0.1909181,0.00004726713,0.00004665156,7.404903e-7,0.003427894,0.05545863,0.003916671,0.4566226,0.0006113551,0.2883904],"study_design_scores_gemma":[0.0007934211,0.00008484738,0.3315332,0.000008554779,0.00004894476,9.03903e-7,0.000207704,0.2786482,0.0004949842,0.3864006,0.001425694,0.0003529857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712104,0.0001842474,0.01365805,0.00004887598,0.0000426504,0.0001475736,0.00002105997,0.00001870893,0.01466838],"genre_scores_gemma":[0.9966469,0.00001646892,0.002974457,0.00005948729,0.00006640169,0.000001757365,0.0001540336,0.000001879996,0.00007861922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2880374,"threshold_uncertainty_score":0.2604205,"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."}}