{"id":"W2748238330","doi":"10.1080/02626667.2019.1624922","title":"A hybrid optimization approach for efficient calibration of computationally intensive hydrological models","year":2019,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Polytechnique Montréal; Group for Research in Decision Analysis; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Computer science; Environmental science; Mathematical optimization; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183885,0.0001433218,0.0002567396,0.00006830248,0.0003973329,0.00004132084,0.0003774097,0.00007616331,0.0005667335],"category_scores_gemma":[0.0001022643,0.00009209129,0.000115325,0.0002011862,0.0007541426,0.0002772821,0.0002280962,0.0001586839,0.00001198289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004668908,"about_ca_system_score_gemma":0.00001187279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005405116,"about_ca_topic_score_gemma":3.356856e-7,"domain_scores_codex":[0.9982923,0.000132949,0.0003777021,0.000397168,0.0004430935,0.0003567521],"domain_scores_gemma":[0.9993705,0.0001588208,0.0002526436,0.00008498014,0.00004885624,0.00008421493],"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":[0.0000869855,0.0001166274,0.005695928,0.000004236942,0.00001535569,0.000002249851,0.0001303501,0.991967,0.0001763174,0.001385362,0.0003048818,0.0001146719],"study_design_scores_gemma":[0.0004588168,0.000833004,0.0007688259,0.000003460825,0.00001764342,0.00004480643,0.00007484957,0.9861355,0.00006599438,0.01144392,0.00003622958,0.0001169339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4769973,0.00001640049,0.5176827,0.0007398208,0.0000875016,0.0002981712,0.000002483396,0.00001610636,0.004159557],"genre_scores_gemma":[0.9520643,0.0000138371,0.04664018,0.0011628,0.0000322329,0.0000168418,0.000009672166,0.000003953429,0.00005615922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.475067,"threshold_uncertainty_score":0.6205335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820387489993074,"score_gpt":0.2401983021246017,"score_spread":0.211994427224671,"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."}}