{"id":"W3047493908","doi":"10.1029/2019wr027009","title":"Automatic Model Structure Identification for Conceptual Hydrologic Models","year":2020,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hydrograph; Identification (biology); Hydrological modelling; Data mining; Modular design; Heuristic; Conceptual model; Mathematical optimization; Machine learning; Surface runoff; Artificial intelligence; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005913108,0.0001261903,0.0001568233,0.00005063483,0.0005044416,0.00005817066,0.0005042922,0.00009326403,0.0007425157],"category_scores_gemma":[0.00005026777,0.00008382902,0.00004947804,0.0001409102,0.0006737736,0.0002160777,0.000646465,0.0002234556,0.000471486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005012383,"about_ca_system_score_gemma":0.00000227898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003013843,"about_ca_topic_score_gemma":0.000009466753,"domain_scores_codex":[0.9981187,0.0001572954,0.0002144914,0.0004724363,0.0004597574,0.0005773468],"domain_scores_gemma":[0.9995654,0.00005553408,0.00002761629,0.0002229153,0.00001862308,0.0001098647],"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":[0.0001878818,0.00006203433,0.00199989,0.00009715849,0.00008318992,0.000009358921,0.06725288,0.8419302,0.0659069,0.0007223031,0.0196615,0.002086752],"study_design_scores_gemma":[0.0003036917,0.0001207184,0.0001375005,0.000002114223,0.00001107148,4.774739e-7,0.0003872743,0.957444,0.00583513,0.02666625,0.008974164,0.000117625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849243,0.0000292538,0.004739406,0.007242152,0.00002045137,0.0006743951,0.00001648829,0.00008904642,0.002264517],"genre_scores_gemma":[0.9971656,0.000008223836,0.0005527745,0.000705911,0.00004948961,0.0001238371,0.00002795085,0.00001591439,0.001350364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1155138,"threshold_uncertainty_score":0.8130026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09567251240638361,"score_gpt":0.3115887071267877,"score_spread":0.215916194720404,"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."}}