{"id":"W4244197432","doi":"10.21203/rs.3.rs-396290/v1","title":"An Improved Calibration and Uncertainty Analysis Approach Using a Multicriteria Sequential Algorithm for Hydrological Modeling","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; United Nations Development Programme","keywords":"GLUE; Calibration; Computer science; Uncertainty analysis; Reliability (semiconductor); Algorithm; Propagation of uncertainty; Hydrological modelling; Mathematical optimization; Sensitivity analysis; Data mining; Mathematics; Statistics; Simulation; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003186675,0.001051908,0.0009647626,0.00197115,0.0008689906,0.001017358,0.001285724,0.001085353,0.00240253],"category_scores_gemma":[0.00765758,0.0007180774,0.0013018,0.001527432,0.0007438387,0.001709246,0.001590436,0.001426661,0.0003952237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006986085,"about_ca_system_score_gemma":0.002260257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007759912,"about_ca_topic_score_gemma":0.005206186,"domain_scores_codex":[0.9981831,0.0007521803,0.0001065583,0.0003732244,0.0004843486,0.0001006149],"domain_scores_gemma":[0.996494,0.001857483,0.0003477465,0.0003220481,0.0008864855,0.00009223817],"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":[0.0001113132,0.0001278066,0.002367666,0.00008215538,0.0001085322,0.0001048661,0.0001786389,0.7652571,0.008243376,0.01368553,0.0008952906,0.2088377],"study_design_scores_gemma":[0.000005391767,0.00001367609,0.0001210083,0.000002762815,0.000004893451,0.00001715315,0.00000537908,0.9968908,0.0008645102,0.001758509,0.0003093374,0.000006500127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00563574,0.00003912437,0.9937841,0.00003445904,0.000008338938,0.00002053862,0.000008890936,0.0002045146,0.0002642653],"genre_scores_gemma":[0.2365708,0.00009022707,0.7619782,0.0000684261,0.00004150427,0.000164372,0.0000974044,0.0001253851,0.0008636945],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007759912,"threshold_uncertainty_score":0.01685292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09631997848703513,"score_gpt":0.3816378393185387,"score_spread":0.2853178608315036,"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."}}