{"id":"W3175382161","doi":"","title":"Regional hydrological modelling with deep convolutional-recurrent neural networks: A case study in Western Canada","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting 2020","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Convolutional neural network; Geography; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004141343,0.0006768352,0.000462206,0.0004780194,0.001472201,0.001602638,0.00130948,0.000826822,0.001634511],"category_scores_gemma":[0.001284085,0.0003208692,0.0005705014,0.001937692,0.0005985099,0.0005734296,0.0004101571,0.0007787736,0.0002363919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01636054,"about_ca_system_score_gemma":0.01581236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9919871,"about_ca_topic_score_gemma":0.9930462,"domain_scores_codex":[0.9997795,0.00002481289,0.00001050369,0.00005397269,0.00005291312,0.00007825706],"domain_scores_gemma":[0.999552,0.000117631,0.000026378,0.00002537764,0.0002288309,0.00004991607],"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":[0.000200844,0.0001779913,0.03905712,0.00008586697,0.0001229737,0.0006415674,0.0003645952,0.9118996,0.00180905,0.002003732,0.00693305,0.03670357],"study_design_scores_gemma":[0.00003356901,0.00001253958,0.01447718,0.00001114379,0.00004117298,0.00002613522,0.0004303378,0.9819914,0.000765369,0.0004788237,0.001703892,0.00002843203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798403,0.0003795903,0.007040325,0.001025609,0.00003027063,0.00004708396,0.002904856,0.001040455,0.007691462],"genre_scores_gemma":[0.9885684,0.0002093098,0.005563312,0.00004975367,0.000006912341,0.00001000899,0.001218814,0.00007571675,0.004297768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01636054,"threshold_uncertainty_score":0.1187046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02652545876739772,"score_gpt":0.2191288647992566,"score_spread":0.1926034060318589,"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."}}