{"id":"W2928248204","doi":"10.3390/w11050977","title":"Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Climate variability and models","field":"Environmental Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; Canada Excellence Research Chairs, Government of Canada; National Natural Science Foundation of China","keywords":"Downscaling; Environmental science; Precipitation; Climatology; Quantitative precipitation forecast; Meteorology; Quantitative precipitation estimation; Artificial neural network; Convolutional neural network; Computer science; Artificial intelligence; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002222808,0.00007666124,0.00007503832,0.00001183251,0.0001024289,0.00003141367,0.00004406864,0.00005305439,0.000490722],"category_scores_gemma":[0.000003441177,0.00006039513,0.00002111316,0.00002840788,0.00006254591,0.0003686559,0.0001183026,0.00006710865,0.00004819192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008577708,"about_ca_system_score_gemma":0.000002774551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001397101,"about_ca_topic_score_gemma":0.00003166804,"domain_scores_codex":[0.9992786,0.00003566904,0.000135344,0.0002220662,0.0001285254,0.0001997696],"domain_scores_gemma":[0.9998041,0.00001843548,0.00001847689,0.0001119112,0.00000515338,0.00004192174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004004562,0.00002330322,0.7566835,0.00001624514,0.000004831035,6.72199e-7,0.0003787078,0.114438,0.1276466,0.00001195769,0.00002642772,0.0007295998],"study_design_scores_gemma":[0.0002158505,0.00004901428,0.4078492,0.000008298815,0.00001480149,0.000006374896,0.0000173721,0.5903971,0.001058555,0.0002866425,0.00001174511,0.00008505145],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982687,0.000006331017,0.0006671528,0.00005265116,0.0003206472,0.000272489,0.000004641716,0.00003120412,0.0003762032],"genre_scores_gemma":[0.999218,0.000001279052,0.0004199084,0.00004739678,0.00005812767,0.000006517767,0.000041763,0.000007648316,0.0001993055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4759591,"threshold_uncertainty_score":0.5373061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01504715932005722,"score_gpt":0.2146025138374212,"score_spread":0.199555354517364,"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."}}