{"id":"W3166730726","doi":"","title":"Extreme Value Analysis of Ground Snow Load in Canada","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Snow; Value (mathematics); Environmental science; Geography; Meteorology; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006221735,0.0001297624,0.0003585388,0.00009598866,0.00003750707,0.000008131961,0.0002526483,0.00007584688,0.0003921538],"category_scores_gemma":[0.0001785153,0.0001253215,0.00009488315,0.001111041,0.00005258507,0.000116926,0.00007925806,0.000157401,0.0002009414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005317353,"about_ca_system_score_gemma":0.0001430907,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9956416,"about_ca_topic_score_gemma":0.9984519,"domain_scores_codex":[0.9984052,0.00007236415,0.0004217653,0.000338005,0.000439029,0.0003236095],"domain_scores_gemma":[0.9991074,0.0002682993,0.0002072147,0.000320684,0.00001308988,0.0000832743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006247676,0.00002273264,0.6619081,0.00000285887,0.0001002139,0.00001699774,0.0001244889,0.3367826,0.0007177147,0.000004719537,0.00006368404,0.0002495797],"study_design_scores_gemma":[0.0001587076,0.00001207487,0.9775581,0.00001502525,0.0002380288,9.245268e-7,0.0001169691,0.02100742,0.0003553627,0.00005652706,0.0003397099,0.0001411339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.933376,0.0000404843,0.000003193825,0.00009978684,0.00006461496,0.00006123716,0.000003387466,0.000008119983,0.06634322],"genre_scores_gemma":[0.9991357,0.000008092209,0.000184111,0.0001938125,0.000008889004,0.000002260266,0.00001098808,0.000006931224,0.0004492452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3157752,"threshold_uncertainty_score":0.5110464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137120950232417,"score_gpt":0.2102484150286519,"score_spread":0.1988772055263277,"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."}}