{"id":"W4393110402","doi":"10.2139/ssrn.4770426","title":"Hydrologic Model Calibration Approaches for Highly Regulated River Basin: A Comprehensive Assessment","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Calibration; Structural basin; Hydrological modelling; Drainage basin; Environmental science; Hydrology (agriculture); Water resource management; Remote sensing; Environmental resource management; Geography; Computer science; Geology; Cartography; Climatology; Geomorphology; Statistics; Mathematics; Geotechnical engineering","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":[0.005931488,0.0009558394,0.0009004051,0.001287024,0.000466667,0.001757055,0.001613972,0.001261539,0.001250912],"category_scores_gemma":[0.007981419,0.0005582119,0.001144946,0.00151897,0.0004591409,0.001711192,0.001270045,0.001099994,0.0002561036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007890351,"about_ca_system_score_gemma":0.001826752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01020277,"about_ca_topic_score_gemma":0.007992398,"domain_scores_codex":[0.9986606,0.0006463539,0.00007372908,0.0001393607,0.0004072205,0.00007270905],"domain_scores_gemma":[0.9966686,0.001769985,0.0003060329,0.0005174103,0.0006589942,0.000078825],"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":[0.00006570945,0.0001425634,0.008756868,0.0001162113,0.0002452194,0.00005911592,0.0000478692,0.9375644,0.001522108,0.002749342,0.000689962,0.04804067],"study_design_scores_gemma":[0.00002053226,0.00006512872,0.004503881,0.00004924343,0.0001010866,0.00002713382,0.00003925999,0.9882979,0.001752566,0.004038421,0.001081705,0.00002317353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4344445,0.003910711,0.5488758,0.0008901972,0.00006949047,0.0002568344,0.001457198,0.001467699,0.008627594],"genre_scores_gemma":[0.9249132,0.002360607,0.06954467,0.0001228274,0.00005354027,0.0001360871,0.001319441,0.0002788261,0.001270865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01020277,"threshold_uncertainty_score":0.03136903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02985663759083674,"score_gpt":0.2532356028659285,"score_spread":0.2233789652750918,"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."}}