{"id":"W2054473854","doi":"10.1175/jhm-d-14-0116.1","title":"Reconstruction of the Spring 2011 Richelieu River Flood by Two Regional Climate Models and a Hydrological Model","year":2014,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"U.S. Department of Energy","keywords":"Flood myth; Snowmelt; Precipitation; Environmental science; Spring (device); Climatology; Climate model; Return period; Hydrograph; Climate change; Hydrology (agriculture); Snow; Hydrometeorology; Hydrological modelling; Water year; Drainage basin; Meteorology; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000837868,0.0001248938,0.0003085234,0.00006344542,0.0001453059,0.000004318709,0.0002524778,0.00009718295,0.00008385378],"category_scores_gemma":[0.00002905957,0.00007952088,0.00009695053,0.00006777675,0.0007989521,0.0002276673,0.0003333038,0.0002474891,0.00001316085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000258439,"about_ca_system_score_gemma":0.000003516934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004641155,"about_ca_topic_score_gemma":0.00001667893,"domain_scores_codex":[0.9988015,0.0001874199,0.0003866102,0.0001861932,0.0001852159,0.0002530826],"domain_scores_gemma":[0.9993132,0.00007163281,0.0003962209,0.0001494552,0.00001306056,0.00005644259],"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":[0.0008348646,0.0003957398,0.317332,0.00003627429,0.0004450916,0.00001482552,0.001078713,0.6287494,0.03330398,0.003930768,0.005234874,0.008643442],"study_design_scores_gemma":[0.002914958,0.001105528,0.04960019,0.00002253848,0.0003258854,0.0007218341,0.00003510674,0.7975184,0.000996081,0.1449921,0.00145586,0.0003115902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923356,0.0001405276,0.003875546,0.001776918,0.0001520902,0.00006782651,0.000001708222,0.000006184755,0.001643619],"genre_scores_gemma":[0.9966671,0.0003397078,0.002285894,0.0006136197,0.00002713518,0.000002270585,2.153763e-7,0.0000063464,0.00005763493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2677318,"threshold_uncertainty_score":0.3242767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311851107678741,"score_gpt":0.211694953954576,"score_spread":0.1985764428777886,"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."}}