{"id":"W2274651272","doi":"10.1002/jwmg.1037","title":"Predicting spatial variation in grizzly bear abundance to inform conservation","year":2016,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; Ministry of Forests; Cochrane","funders":"Parks Canada; Government of Alberta","keywords":"Ursus; Grizzly Bears; Geography; Context (archaeology); Population; Vegetation (pathology); Ecology; Environmental resource management; Physical geography; Environmental science; Demography; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004003254,0.0003742998,0.0005291712,0.001784379,0.0002225884,0.0009284645,0.0007243462,0.0003404148,0.0007811937],"category_scores_gemma":[0.006491061,0.0003112247,0.0007153183,0.001820341,0.0004089185,0.0006040832,0.0004181516,0.0004190726,0.0001168705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007700796,"about_ca_system_score_gemma":0.0007673454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1224952,"about_ca_topic_score_gemma":0.1352958,"domain_scores_codex":[0.9991049,0.0004241753,0.00003922226,0.0002913646,0.00009866317,0.00004168139],"domain_scores_gemma":[0.9959632,0.002701554,0.0005512318,0.0003417564,0.0003556304,0.00008668943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005579176,0.00002619769,0.9509571,0.0002032994,0.002426205,0.00003362994,0.000113331,0.02028962,0.000565738,0.0003042243,0.001014119,0.02401067],"study_design_scores_gemma":[0.00003051957,0.00009077731,0.8872633,0.0001652966,0.001870153,0.000054951,0.0005815255,0.1048818,0.0005424276,0.002276314,0.002215598,0.00002733389],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964062,0.005535764,0.02520061,0.0008551001,0.0000427489,0.00004859647,0.001989553,0.0002738954,0.001991796],"genre_scores_gemma":[0.994356,0.000594563,0.004266267,0.00006610792,0.00001256742,0.00001566672,0.0005249646,0.00001186058,0.0001519445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1224952,"threshold_uncertainty_score":0.2435645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009469887446279763,"score_gpt":0.2177938942378657,"score_spread":0.2083240067915859,"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."}}