{"id":"W4212789837","doi":"10.5194/hess-26-795-2022","title":"Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling","year":2022,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Computer science; Sensitivity (control systems); Hydrological modelling; Climatology; Temporal scales; Environmental science; Drainage basin; Geology; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003458159,0.001194995,0.000444738,0.0005883751,0.0003148901,0.001004861,0.001162107,0.001196093,0.001505731],"category_scores_gemma":[0.006679518,0.0003190798,0.000525525,0.0004206831,0.0005314914,0.001173638,0.0007354586,0.001013127,0.0001763731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002635253,"about_ca_system_score_gemma":0.001465098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03395048,"about_ca_topic_score_gemma":0.03126917,"domain_scores_codex":[0.9994766,0.0002181523,0.00004797645,0.0001306159,0.00007392455,0.00005278728],"domain_scores_gemma":[0.9977005,0.001349756,0.0001593825,0.0001838983,0.000492653,0.0001138258],"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":[0.0002443507,0.0001003439,0.00646882,0.00004345829,0.0001062931,0.00004463324,0.00002378041,0.9667076,0.001281569,0.0005813469,0.000468963,0.02392874],"study_design_scores_gemma":[0.000007824837,0.00002893974,0.0004074709,0.000004791847,0.000007367527,0.000002505035,0.000006168881,0.9984883,0.0008231167,0.0001862092,0.00003462702,0.00000258032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371673,0.0008557228,0.05504707,0.0008981,0.0001279134,0.0001061891,0.0006246866,0.001566284,0.003606692],"genre_scores_gemma":[0.9894297,0.00006897154,0.009620659,0.00007804774,0.000007317318,0.00002029681,0.0003457879,0.00003226951,0.0003970359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03395048,"threshold_uncertainty_score":0.06750572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946648598697775,"score_gpt":0.2586038152592142,"score_spread":0.2191373292722364,"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."}}