{"id":"W2786782883","doi":"","title":"Data Fusion Snow Water Estimates for British Columbia, Canada: A Regional Perspective","year":2017,"lang":"en","type":"article","venue":"97th American Meteorological Society Annual Meeting","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Snow; Perspective (graphical); Geography; Physical geography; Meteorology; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001816357,0.0009720123,0.0008835631,0.00402128,0.002346562,0.003190434,0.001506368,0.0008415671,0.002582668],"category_scores_gemma":[0.003753755,0.0004010278,0.0005921072,0.01229073,0.0005851419,0.001553549,0.001094618,0.00110261,0.0007148628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02188813,"about_ca_system_score_gemma":0.06250343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.997389,"about_ca_topic_score_gemma":0.9984651,"domain_scores_codex":[0.998887,0.00006615752,0.00007121872,0.0001519504,0.000576504,0.0002472752],"domain_scores_gemma":[0.9930819,0.0001962909,0.0002232266,0.0001801685,0.006004138,0.0003142995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000863492,0.0002937584,0.3309152,0.001114242,0.001265634,0.0007150651,0.001754246,0.05596827,0.008373178,0.006463795,0.2079325,0.3843407],"study_design_scores_gemma":[0.0001491351,0.00006630245,0.7363299,0.001313741,0.0009822805,0.0001850194,0.00506081,0.04701138,0.005037367,0.002539719,0.2010283,0.0002961297],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5581418,0.02864674,0.01308948,0.03200134,0.0008737065,0.0004023825,0.2533392,0.001701853,0.1118035],"genre_scores_gemma":[0.8917234,0.01023846,0.0218694,0.001517462,0.0001646948,0.0001109131,0.05792074,0.0004209108,0.01603397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02188813,"threshold_uncertainty_score":0.1588103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957201436431061,"score_gpt":0.26202235120661,"score_spread":0.2224503368422994,"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."}}