{"id":"W7098707542","doi":"","title":"P1.5 McGILL ALGORITHM FOR PRECIPITATION NOWCASTING BY LAGRANGIAN EXTRAPOLATION (MAPLE) APPLIED TO THE SOUTH KOREAN RADAR NETWORK. PART 2: REAL-TIME VERIFICATION FOR THE SUMMER SEASON","year":2015,"lang":"en","type":"article","venue":"","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nowcasting; Extrapolation; Precipitation; Radar; Quantitative precipitation forecast; Numerical weather prediction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009057824,0.0001674554,0.0001507674,0.00004093774,0.0005100601,0.0001718337,0.000205612,0.00005976001,0.00002677367],"category_scores_gemma":[0.00003227822,0.0001132683,0.00008071437,0.0001925958,0.00002564098,0.0004087567,0.00002407279,0.00004540456,0.00009614146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003961376,"about_ca_system_score_gemma":0.00001163883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006743657,"about_ca_topic_score_gemma":0.0004420119,"domain_scores_codex":[0.9989763,0.000006040481,0.0002889531,0.000257832,0.0001202663,0.0003506287],"domain_scores_gemma":[0.9993115,0.0001336165,0.0002024688,0.0002270762,0.00009863407,0.00002671141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003879815,0.00008041755,0.002269098,0.00007577009,0.0001574849,8.203181e-8,0.001662083,0.009176527,0.0006541819,0.02029687,0.6027098,0.3625297],"study_design_scores_gemma":[0.0008251749,0.00001619499,0.002961768,0.00001213118,0.0001441125,1.323127e-7,0.001002862,0.1792607,0.00005269005,0.0007034984,0.8147694,0.0002513232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07275186,0.0001847742,0.7992438,0.02278381,0.002826823,0.01112192,0.0006756793,0.001050544,0.0893608],"genre_scores_gemma":[0.9604319,0.00001394662,0.01705127,0.004266222,0.007742024,0.001432021,0.003073237,0.000141834,0.005847587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.88768,"threshold_uncertainty_score":0.4618946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890496638643238,"score_gpt":0.2294538271459889,"score_spread":0.1905488607595565,"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."}}