{"id":"W3108290395","doi":"10.1139/cjce-2020-0275","title":"Using multiple objective calibrations to explore uncertainty in extreme event modeling","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Kerr Wood Leidal Associates (Canada)","funders":"","keywords":"Watershed; Calibration; Flood myth; Environmental science; Event (particle physics); Extreme value theory; Statistics; Hydrology (agriculture); 100-year flood; Meteorology; Computer science; Mathematics; Geography; Geology; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.006027991,0.001195601,0.0006998485,0.001691711,0.0003529815,0.001183109,0.0008624031,0.000854339,0.0008847261],"category_scores_gemma":[0.01718173,0.0006066369,0.0007805437,0.001164717,0.0006120277,0.001471733,0.001177038,0.001105433,0.00008152539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320571,"about_ca_system_score_gemma":0.0008240498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005471931,"about_ca_topic_score_gemma":0.003256631,"domain_scores_codex":[0.9982376,0.001107115,0.00005841801,0.0002145381,0.0002839307,0.00009839831],"domain_scores_gemma":[0.9914112,0.006974204,0.000710255,0.0004127922,0.000419965,0.00007161632],"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":[0.00001863597,0.00001957626,0.001204556,0.000008344933,0.00003165947,0.00001204719,0.00001993294,0.99322,0.0002491538,0.0009077214,0.00002819396,0.004280165],"study_design_scores_gemma":[0.000004699662,0.00001887845,0.0006784541,0.0000034911,0.00000374405,0.000004234775,0.000009917667,0.9973055,0.0005352725,0.001365855,0.00006074966,0.000009217397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3918225,0.0001511893,0.6032984,0.0001703898,0.00001329897,0.000135979,0.0002077232,0.0004787391,0.003721823],"genre_scores_gemma":[0.9354671,0.00005368032,0.06376125,0.00003022996,0.000006360658,0.0001028476,0.0001862036,0.00006231636,0.0003299646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006027991,"threshold_uncertainty_score":0.03187948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07606031724471765,"score_gpt":0.2247060823731879,"score_spread":0.1486457651284703,"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."}}