{"id":"W2944218667","doi":"10.1080/07055900.2019.1598843","title":"Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016","year":2019,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Ministry of Environment; Ontario Ministry of Natural Resources and Forestry","keywords":"Snowpack; Snow; Environmental science; Water equivalent; Arctic; Climate change; Physical geography; Climatology; Permafrost; Snow cover; Latitude; Flood myth; Geography; Meteorology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001634112,0.0009500545,0.0006263801,0.01014638,0.002320152,0.001595163,0.001552915,0.0002821,0.004091715],"category_scores_gemma":[0.005185377,0.0004477194,0.0009772629,0.02218198,0.0003350558,0.0008176469,0.001147369,0.0006319227,0.001724388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03794743,"about_ca_system_score_gemma":0.06962454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971164,"about_ca_topic_score_gemma":0.9983285,"domain_scores_codex":[0.9980868,0.00005476752,0.0001366878,0.0002051966,0.001236095,0.000280398],"domain_scores_gemma":[0.9849501,0.0001325086,0.0004180705,0.0003242686,0.01367973,0.0004953492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003844869,0.00009632023,0.3498048,0.00161399,0.0004815529,0.0002870036,0.001382234,0.0050094,0.001719333,0.002594519,0.5081106,0.1285158],"study_design_scores_gemma":[0.00002928254,0.00001487348,0.7082286,0.0003419499,0.0001079312,0.00006803371,0.0008012197,0.002136733,0.0009704712,0.0001420473,0.2870909,0.00006787837],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05595828,0.001674339,0.001355853,0.0006715358,0.0002120148,0.0002187764,0.9256486,0.0003773729,0.01388328],"genre_scores_gemma":[0.1011836,0.002467083,0.007081873,0.0002287035,0.00005261717,0.0002591635,0.8793486,0.0001586849,0.009219695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03794743,"threshold_uncertainty_score":0.2753292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03777710129901846,"score_gpt":0.2292787398928069,"score_spread":0.1915016385937884,"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."}}