{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002891975,0.0005465447,0.00030793,0.0002590508,0.0001778277,0.0002914937,0.0005357162,0.0003664103,0.0005593139],"category_scores_gemma":[0.0007975218,0.000235278,0.0003885468,0.0002619277,0.0001880858,0.0007562599,0.0004143692,0.000525958,0.0001252888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004255601,"about_ca_system_score_gemma":0.000670036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0192905,"about_ca_topic_score_gemma":0.0180548,"domain_scores_codex":[0.9999249,0.000009389782,0.00000421776,0.00002480729,0.00001790552,0.00001880637],"domain_scores_gemma":[0.9998571,0.00004629528,0.0000240223,0.00001894309,0.00004268107,0.00001086995],"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":[0.00009621575,0.0001060012,0.006121845,0.00003096004,0.00008087851,0.00006559249,0.00002431117,0.9041291,0.01083038,0.0007287373,0.0008451284,0.07694095],"study_design_scores_gemma":[0.000002045876,0.000005020849,0.0003459594,5.082885e-7,0.000003715586,0.000001416272,7.67276e-7,0.9989777,0.000545922,0.00008072902,0.00003527802,0.000001097262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7135406,0.0007656292,0.279399,0.0004823635,0.000132107,0.00002850173,0.0002652672,0.002036606,0.003349924],"genre_scores_gemma":[0.9840215,0.0001148681,0.01488744,0.00004046264,0.00002183133,0.00001049362,0.000168093,0.00001772087,0.0007175452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0192905,"threshold_uncertainty_score":0.03835642,"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."}}