{"id":"W4401390913","doi":"10.22541/essoar.172304428.82707157/v1","title":"Improving Physics-informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Army Corps of Engineers; National Oceanic and Atmospheric Administration","keywords":"Differentiable function; Econometrics; Physics; Computer science; Statistical physics; Theoretical physics; Mathematics; Pure mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000502118,0.0008939323,0.0004678074,0.0003104089,0.000181759,0.0007970527,0.0008510724,0.000854848,0.002334216],"category_scores_gemma":[0.0026338,0.0003482716,0.0005325211,0.0004017379,0.0003530971,0.00165905,0.0009030685,0.001493031,0.0007554837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005065198,"about_ca_system_score_gemma":0.0007186202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005996136,"about_ca_topic_score_gemma":0.006778792,"domain_scores_codex":[0.999899,0.00002303945,0.000006403024,0.00004085279,0.0000155072,0.00001514221],"domain_scores_gemma":[0.9995232,0.0002318108,0.00005347153,0.00009209354,0.00006210667,0.00003730658],"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":[0.0000779122,0.00004446243,0.00182322,0.0000450781,0.00004484459,0.00004977021,0.00002773582,0.9388279,0.003271544,0.003110914,0.002962125,0.04971449],"study_design_scores_gemma":[0.000003164048,0.000004169826,0.0001384513,0.000002114661,0.0000024848,0.000004022741,0.000001941053,0.9971621,0.0003701184,0.002137819,0.000171526,0.000002205755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1564613,0.0008300283,0.8315628,0.00137558,0.0001480887,0.00002929573,0.00124724,0.004871457,0.003474253],"genre_scores_gemma":[0.9196338,0.0003009702,0.07419336,0.0003505108,0.0001020605,0.00003117193,0.001792095,0.0003027046,0.003293361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005996136,"threshold_uncertainty_score":0.01192248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05577615197060247,"score_gpt":0.229594200731834,"score_spread":0.1738180487612315,"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."}}