{"id":"W6888485237","doi":"10.18739/a24m91c3t","title":"Whitehorse, Yukon modified ABoVE: Landsat-derived Annual Dominant Land Cover 1984-2054","year":2023,"lang":"en","type":"dataset","venue":"UC Santa Barbara","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Evergreen; Evergreen forest; Flammable liquid; Vegetation (pathology); Montane ecology; Boreal; Elevation (ballistics)","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":[0.000153966,0.0005325371,0.0002885591,0.001068225,0.0002871734,0.0005702309,0.0005400219,0.0002665328,0.01223182],"category_scores_gemma":[0.0003625156,0.0002175706,0.0005010398,0.003190083,0.0001414325,0.0005152342,0.0003507827,0.0003588613,0.00730939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096034,"about_ca_system_score_gemma":0.002162831,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3132652,"about_ca_topic_score_gemma":0.4155937,"domain_scores_codex":[0.9998779,0.000007271001,0.00001381314,0.00004539252,0.00003026099,0.00002523419],"domain_scores_gemma":[0.9998314,0.000005805021,0.00001554487,0.00002595143,0.0001116875,0.000009588925],"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":[0.0002079899,0.0001428444,0.09844284,0.001235549,0.0004950375,0.0004576696,0.0003296405,0.02500929,0.00375628,0.002997309,0.8065741,0.06035149],"study_design_scores_gemma":[0.0001522445,0.00004212467,0.3759731,0.0002251191,0.0001885863,0.000302151,0.001048873,0.02321052,0.002269656,0.001444005,0.5950239,0.000119712],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0416583,0.000237132,0.001921318,0.0001700693,0.0001158898,0.00008392812,0.94761,0.0007025232,0.007500777],"genre_scores_gemma":[0.1046951,0.0002479517,0.004549166,0.0001183791,0.00001650835,0.000214353,0.8800244,0.0002803269,0.009853792],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6867348,"threshold_uncertainty_score":0.6228836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848274466006914,"score_gpt":0.2761538734939476,"score_spread":0.2576711288338784,"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."}}