{"id":"W2023005676","doi":"10.3137/ao.v450101","title":"A Canadian precipitation analysis (CaPA) project: Description and preliminary results","year":2007,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":234,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Rain gauge; Precipitation; Radar; Environmental science; Meteorology; Quantitative precipitation estimation; Interpolation (computer graphics); Quantitative precipitation forecast; Range (aeronautics); Climatology; Geography; Computer science; Geology; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.003915614,0.002090071,0.0007985907,0.002812819,0.002669838,0.00208265,0.00265569,0.0005990501,0.01008926],"category_scores_gemma":[0.004410245,0.0005300531,0.0009460665,0.00520547,0.0007220756,0.0007824863,0.001277065,0.0007073052,0.003900274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0107961,"about_ca_system_score_gemma":0.03261429,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.956822,"about_ca_topic_score_gemma":0.9085466,"domain_scores_codex":[0.9963696,0.0005454588,0.0001092529,0.0004167263,0.002095869,0.0004631646],"domain_scores_gemma":[0.9959954,0.0002059368,0.00008848077,0.000341336,0.002737214,0.0006315743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002843712,0.002831345,0.08643758,0.001412527,0.0004981888,0.0004388687,0.001184138,0.07078137,0.02684443,0.01023468,0.1996887,0.5968045],"study_design_scores_gemma":[0.002822399,0.001348704,0.3521819,0.0003092703,0.0005894857,0.000373242,0.00153113,0.1936369,0.03824424,0.00280283,0.4056354,0.00052448],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2891094,0.00357101,0.1524745,0.002723758,0.0004799383,0.02429759,0.3787375,0.02388926,0.124717],"genre_scores_gemma":[0.3690691,0.00244013,0.3334358,0.000445358,0.0001435644,0.008257528,0.2404561,0.002690697,0.04306171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04317796,"threshold_uncertainty_score":0.08686441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02309136220067789,"score_gpt":0.2339722930090123,"score_spread":0.2108809308083344,"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."}}