{"id":"W4220824689","doi":"10.1016/j.jhydrol.2022.127766","title":"Large-sample study of uncertainty of hydrological model components over North America","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canarie","keywords":"Environmental science; Sample (material); Hydrology (agriculture); Hydrological modelling; Geology; Climatology; Geotechnical 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":[0.005141628,0.0003139048,0.0004489982,0.0007449456,0.0008787476,0.0006134178,0.0009259759,0.0007603785,0.001009398],"category_scores_gemma":[0.02828069,0.000373573,0.0005433148,0.0007815235,0.0009983092,0.001068267,0.001040047,0.001033078,0.0001515476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007773674,"about_ca_system_score_gemma":0.0005834322,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02290976,"about_ca_topic_score_gemma":0.03485363,"domain_scores_codex":[0.9969162,0.002073193,0.0001441751,0.0005054597,0.0002522167,0.0001088636],"domain_scores_gemma":[0.952707,0.03975205,0.002327102,0.003062473,0.00139841,0.0007529258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005976753,0.0009143817,0.9777219,0.00002439272,0.0007591161,0.0003034697,0.001483792,0.007549237,0.0008757903,0.0003527182,0.0005686951,0.008848713],"study_design_scores_gemma":[0.00009678243,0.0004266915,0.9373966,0.00001081025,0.0002643653,0.000373276,0.002496222,0.05678841,0.0005956384,0.0005771804,0.0009323338,0.00004167836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993306,0.0000192874,0.0004402253,0.00003058095,0.000001815678,0.000005393562,0.0000948173,0.000006086347,0.00007128983],"genre_scores_gemma":[0.9993396,0.000009913776,0.0002258089,0.00001381103,0.000003719372,0.000007799146,0.0003304865,0.000004775782,0.00006395717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9770902,"threshold_uncertainty_score":0.04555285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193255017935153,"score_gpt":0.2447239815059373,"score_spread":0.225398479712422,"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."}}