{"id":"W2323511685","doi":"10.1109/tgrs.2015.2507779","title":"Multimodel Prediction of Monsoon Rain Using Dynamical Model Selection","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Climate variability and models","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Space Applications Centre","keywords":"Climatology; Precipitation; Environmental science; Forecast skill; Model output statistics; Monsoon; Meteorology; Quantitative precipitation forecast; Range (aeronautics); Mean squared error; Global Forecast System; Weather forecasting; Numerical weather prediction; Computer science; Mathematics; Statistics; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004092722,0.0005081701,0.0005415714,0.0003935683,0.0003007399,0.0003943438,0.000418967,0.0003245525,0.0005007123],"category_scores_gemma":[0.000820722,0.0002659844,0.0007387073,0.000340847,0.0001017341,0.0003872769,0.000325996,0.0005120612,0.0001164909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002742363,"about_ca_system_score_gemma":0.0005377957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.011272,"about_ca_topic_score_gemma":0.01069332,"domain_scores_codex":[0.9998724,0.00004242601,0.000007574294,0.00003208371,0.00002899769,0.00001642099],"domain_scores_gemma":[0.9997645,0.0001225923,0.000031162,0.0000170153,0.00005145413,0.00001325249],"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":[0.00003930033,0.00004736155,0.003978793,0.00002067568,0.0001053678,0.00003887015,0.00001704248,0.9691556,0.002253389,0.0005187607,0.0003427704,0.02348198],"study_design_scores_gemma":[0.000002663148,0.00000513918,0.0004453104,5.774497e-7,0.000003165499,0.000002136303,0.000001106182,0.9992247,0.0001658488,0.00007499013,0.0000725059,0.000001833713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4509367,0.0006913779,0.5435714,0.0002674243,0.0001345505,0.00006349928,0.0005963143,0.0009172298,0.002821446],"genre_scores_gemma":[0.9723029,0.0001652888,0.02597499,0.00003323749,0.00003682516,0.00004847825,0.0006751082,0.00002953696,0.0007335495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.011272,"threshold_uncertainty_score":0.02241272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673486855295256,"score_gpt":0.2448184719550159,"score_spread":0.2180836034020633,"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."}}