{"id":"W1991002759","doi":"10.1175/2009jhm1154.1","title":"Simulation of Snow Water Equivalent (SWE) Using Thermodynamic Snow Models in Québec, Canada","year":2009,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Université de Sherbrooke","funders":"Cold Regions Research and Engineering Laboratory; U.S. Army Corps of Engineers; McGill University; WSL-Institut für Schnee- und Lawinenforschung SLF; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Snow; Snowpack; Environmental science; Climatology; Snow cover; Water equivalent; Range (aeronautics); Atmospheric sciences; Scale (ratio); Climate model; Albedo (alchemy); Meteorology; Climate change; Geology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003012358,0.0007133081,0.0002881248,0.0004633006,0.001222529,0.0008804419,0.001119288,0.0006154191,0.002073487],"category_scores_gemma":[0.0007251584,0.0002951295,0.0004443683,0.0007501377,0.000452946,0.0003389601,0.0003011031,0.000430855,0.000169003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01490732,"about_ca_system_score_gemma":0.007956088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9871495,"about_ca_topic_score_gemma":0.9854183,"domain_scores_codex":[0.9998654,0.00002274576,0.000005471105,0.0000300052,0.00002683003,0.00004947808],"domain_scores_gemma":[0.9994849,0.0001315102,0.00003461523,0.00002072044,0.0002477166,0.00008042029],"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":[0.000193646,0.0001759448,0.0553156,0.00004300506,0.00008172858,0.0001862686,0.00008443937,0.9331251,0.001836974,0.0004422349,0.001687067,0.006828008],"study_design_scores_gemma":[0.00004783291,0.00003091687,0.0180455,0.000006383861,0.00001444949,0.000006701301,0.00009887832,0.9807286,0.0004210456,0.00004982984,0.0005340576,0.00001584394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928458,0.00008875612,0.001499678,0.0001269864,0.00001031994,0.00004939179,0.0018039,0.000170924,0.003404341],"genre_scores_gemma":[0.9960688,0.00004477524,0.001509473,0.00002510657,0.000001634239,0.00002481725,0.001104455,0.00001376429,0.001207066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01490732,"threshold_uncertainty_score":0.1081607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02710628811643497,"score_gpt":0.2373039548872697,"score_spread":0.2101976667708347,"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."}}