{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003133856,0.0001197152,0.0001269964,0.00008372201,0.0003072693,0.0000144315,0.00005877432,0.00009784443,0.00002330133],"category_scores_gemma":[0.00001028825,0.00008869347,0.00005522231,0.0002597111,0.000391707,0.0003387512,0.000003994156,0.0001014193,0.000005034345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001654575,"about_ca_system_score_gemma":0.00002240335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008925447,"about_ca_topic_score_gemma":0.0001521506,"domain_scores_codex":[0.9988541,0.00004829631,0.0002255554,0.0003816155,0.0002528887,0.0002375608],"domain_scores_gemma":[0.9996153,0.00006389125,0.00006154044,0.0001532861,0.00001790182,0.00008815046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002950172,0.00003980559,0.00001521057,0.000005357045,0.000002786017,3.466506e-7,0.0002233719,0.2527211,0.5370972,0.000003053349,0.000001142909,0.2098612],"study_design_scores_gemma":[0.0002517644,0.00005603455,0.0001730963,0.00006343851,0.00001984854,0.0000270601,0.00003363299,0.9774103,0.02110033,0.0007619653,0.000004047492,0.0000985169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4710472,6.804004e-7,0.5286205,0.00007088244,0.00007182514,0.0000750688,0.00001387532,0.00002433073,0.00007562683],"genre_scores_gemma":[0.902402,0.00003466966,0.09737048,0.0000334163,0.000007326295,1.001512e-7,2.404269e-7,0.000008121217,0.0001436031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7246891,"threshold_uncertainty_score":0.3616815,"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."}}