{"id":"W1111091419","doi":"10.1016/j.jhydrol.2015.08.003","title":"Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA)","year":2015,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":127,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"European Commission","keywords":"Quantitative precipitation estimation; Environmental science; Radar; Meteorology; Precipitation; Quantitative precipitation forecast; Weather radar; Global Precipitation Measurement; Climatology; Computer science; Geography; Telecommunications; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002688817,0.00007842332,0.0002575942,0.001180263,0.00008124459,0.00003254927,0.0002075981,0.00006782043,0.0001642127],"category_scores_gemma":[0.0007763285,0.00005590627,0.0001220636,0.00128748,0.00006790254,0.0004415867,0.000001845342,0.000153445,0.00001327624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003186031,"about_ca_system_score_gemma":0.0003302923,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01844127,"about_ca_topic_score_gemma":0.6075959,"domain_scores_codex":[0.998114,0.0005787307,0.0005863442,0.00009200499,0.0004846937,0.0001442261],"domain_scores_gemma":[0.9983373,0.0003872259,0.0006048603,0.00009940821,0.0004669862,0.0001042122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006027089,0.00002636783,0.6479791,0.000003566512,0.0002394803,0.000003922391,0.006762146,0.3422045,0.00003300041,0.0003979623,0.0003426139,0.001947021],"study_design_scores_gemma":[0.0003718367,0.0003183155,0.8463883,0.000006658051,0.0003986198,0.000006205014,0.0008963435,0.145815,0.00002006162,0.005602255,0.0001164381,0.00005995423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834024,0.0003075146,0.009105129,0.003065113,0.0001920837,0.0001256168,0.00002301034,0.000003134641,0.003775939],"genre_scores_gemma":[0.9926369,0.00001252351,0.007084282,0.0001193957,0.00003665894,7.245697e-7,0.00009752953,0.000001491219,0.00001055232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5891547,"threshold_uncertainty_score":0.988095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0659194257920925,"score_gpt":0.2901420700190869,"score_spread":0.2242226442269944,"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."}}