{"id":"W2017327921","doi":"10.1155/2015/545376","title":"Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization","year":2015,"lang":"en","type":"article","venue":"Advances in Meteorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Calibration; Ideal (ethics); Global optimization; Watershed; Surface runoff; Model parameter; Function (biology); Experimental data; Mathematical optimization; Surface (topology); Nonlinear system; Computer science; Mathematics; Hydrology (agriculture); Algorithm; Statistics; Geology; Ecology; Physics; Machine learning; Geotechnical engineering; Biology","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":[0.0002587423,0.0000810495,0.0001600913,0.00002539914,0.00003243434,0.000001790992,0.0001183147,0.00006532356,0.00008851333],"category_scores_gemma":[0.00004747981,0.00007558714,0.00001687016,0.000173249,0.0006070968,0.0006283532,0.0001492814,0.00004434154,0.00000398873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008084947,"about_ca_system_score_gemma":0.000006341002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282893,"about_ca_topic_score_gemma":0.0001880764,"domain_scores_codex":[0.9992051,0.0001231804,0.0001992461,0.0001892864,0.0001065739,0.0001766519],"domain_scores_gemma":[0.9997493,0.00002748856,0.00008565835,0.0001002847,0.000007476491,0.00002977452],"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":[0.00005449431,0.00002146913,0.03525676,0.000001860337,0.000006299175,0.000002689632,0.0004655807,0.9607918,0.0000415996,0.00292112,0.00004574868,0.0003906306],"study_design_scores_gemma":[0.0007302928,0.0001510784,0.0003268739,0.000002787366,0.00001773693,0.000002919153,0.0004172916,0.9593132,0.0001274109,0.03810013,0.000702089,0.0001082513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5187762,0.0006992234,0.4651802,0.0002637058,0.0003253728,0.0002049936,0.000004370511,0.00002686674,0.01451907],"genre_scores_gemma":[0.9759698,0.0001101927,0.02358126,0.000288337,0.00001251739,0.000007889362,0.000005988867,0.00000352093,0.00002051831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4571936,"threshold_uncertainty_score":0.3082354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02934212926759778,"score_gpt":0.2799088163835415,"score_spread":0.2505666871159437,"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."}}