{"id":"W6944049881","doi":"10.17632/57852x9rrx.2","title":"Annual Snow Timing Index Rasters for the Western US and Alaska, WY2001-2019","year":2020,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snow; Snow cover; Water equivalent; Snow line; Index (typography); Snow field","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004089366,0.0007415236,0.0004582873,0.001972234,0.0003903025,0.000710165,0.0009815051,0.0003564685,0.02309172],"category_scores_gemma":[0.001588676,0.000391568,0.0004508813,0.005904379,0.0001562765,0.0006608104,0.000585735,0.0006500643,0.0142503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009432004,"about_ca_system_score_gemma":0.002175112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1607992,"about_ca_topic_score_gemma":0.21901,"domain_scores_codex":[0.9997837,0.00002373243,0.0000377062,0.00006804472,0.00005661504,0.0000303092],"domain_scores_gemma":[0.9991265,0.00008101502,0.0001123046,0.0001553421,0.0004489798,0.00007575884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007451027,0.00003595036,0.008157355,0.0006006043,0.00008196748,0.00005885664,0.0001397629,0.001320477,0.0004000607,0.0009479031,0.9813059,0.006876637],"study_design_scores_gemma":[0.0002218368,0.00001430707,0.06549744,0.0003404821,0.00006168422,0.00006475088,0.0004111921,0.001904856,0.0009129775,0.001175868,0.9293581,0.0000364173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006724181,0.00002762089,0.00007044482,0.0000198173,0.00001097523,0.00000834535,0.9982265,0.0001727871,0.0007911047],"genre_scores_gemma":[0.001766955,0.00004943003,0.0004316486,0.00001023895,0.000003010737,0.0000476029,0.9969994,0.00003359686,0.0006581802],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1607992,"threshold_uncertainty_score":0.3197265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634041417022853,"score_gpt":0.2406921846956002,"score_spread":0.2143517705253717,"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."}}