{"id":"W2309210120","doi":"10.1071/wf15170","title":"Using Landsat imagery to backcast fire and post-fire residuals in the Boreal Shield of Saskatchewan: implications for woodland caribou management","year":2016,"lang":"en","type":"article","venue":"International Journal of Wildland Fire","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Environment; Cameco (Canada); Calgary Laboratory Services; BP (Canada); Saskatchewan Ministry of Agriculture; Alberta Environment and Protected Areas","funders":"","keywords":"Woodland caribou; Boreal; Critical habitat; Taiga; Habitat; Fire regime; Geography; Population; Environmental science; Disturbance (geology); Fire ecology; Threatened species; Woodland; Thematic Mapper; Ecology; Physical geography; Forestry; Satellite imagery; Ecosystem; Geology; Endangered species; Remote sensing","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":[0.001354264,0.0006348082,0.0002950404,0.001631847,0.0007722548,0.002267777,0.001121081,0.0004727292,0.001344776],"category_scores_gemma":[0.003261828,0.0003421044,0.000486349,0.003153757,0.0004578873,0.001053495,0.0007842929,0.0007128273,0.0002440549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008027274,"about_ca_system_score_gemma":0.009329393,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9267655,"about_ca_topic_score_gemma":0.9770454,"domain_scores_codex":[0.9994822,0.0001479715,0.00004307238,0.00009629982,0.0001030192,0.0001274078],"domain_scores_gemma":[0.9987436,0.0002633538,0.0001849546,0.00009029178,0.0005381698,0.0001797435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001328341,0.0001815452,0.9522134,0.00007304367,0.0002469766,0.0002020215,0.0008225793,0.006310592,0.001556447,0.0002169199,0.002321366,0.03572222],"study_design_scores_gemma":[0.0000184808,0.00002547078,0.9722778,0.0001369656,0.00009418131,0.00004050622,0.0040863,0.02079136,0.0004950208,0.0001953143,0.001802371,0.00003617522],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905012,0.0005886587,0.0008430406,0.0009323643,0.00003814514,0.00007377856,0.003589573,0.00006816768,0.003365079],"genre_scores_gemma":[0.993489,0.0005965887,0.002782419,0.0002430493,0.00001156428,0.00003743362,0.001843692,0.0000278813,0.0009682901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0732345,"threshold_uncertainty_score":0.1473315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361216639230591,"score_gpt":0.2693569293641349,"score_spread":0.255744762971829,"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."}}