{"id":"W2783276865","doi":"10.1007/s11069-017-3150-6","title":"Flood modelling improvement using automatic calibration of two dimensional river software SRH-2D","year":2018,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Natural hazard; Calibration; Flood myth; Hydrogeology; Software; Environmental science; Hydrology (agriculture); Remote sensing; Computer science; Water resource management; Geology; Geography; Geotechnical engineering; Statistics; Mathematics; Oceanography","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":[0.0007635011,0.0007489135,0.0005607642,0.00135327,0.000402262,0.0009902564,0.001265621,0.0006616893,0.01055348],"category_scores_gemma":[0.002456988,0.0005843288,0.0008657859,0.00107867,0.000183131,0.0009736078,0.0008813106,0.0007542475,0.002420254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002733201,"about_ca_system_score_gemma":0.0009006737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004933383,"about_ca_topic_score_gemma":0.004906761,"domain_scores_codex":[0.9995583,0.00006566243,0.00004086279,0.0001198277,0.000168704,0.00004664341],"domain_scores_gemma":[0.9992576,0.0001464074,0.00005692858,0.0001976327,0.0003107423,0.00003057606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005117012,0.0004036829,0.02880848,0.0004423259,0.0002719691,0.0004011645,0.0005874459,0.3344678,0.07152949,0.004337323,0.04199445,0.5162443],"study_design_scores_gemma":[0.00006986073,0.00003858285,0.006962146,0.00001810169,0.0000384515,0.00009310993,0.00004672432,0.9533454,0.02659789,0.001031883,0.01168597,0.00007204019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1181476,0.0001606093,0.7755704,0.0002739171,0.0002514615,0.0001201182,0.004237892,0.09520054,0.006037539],"genre_scores_gemma":[0.5960767,0.0001566138,0.3872764,0.0001268114,0.00003930631,0.0002543277,0.007017533,0.005401237,0.003651197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01055348,"threshold_uncertainty_score":0.0353049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283284203826992,"score_gpt":0.2457895848117649,"score_spread":0.232956742773495,"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."}}