{"id":"W2482939527","doi":"10.1016/j.biocon.2016.06.020","title":"Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies","year":2016,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"Parks Canada","funders":"Yellowstone to Yukon Conservation Initiative; Parks Canada; University of Montana","keywords":"Occupancy; Ursus; Threatened species; Grizzly Bears; Metric (unit); Camera trap; Geography; Scale (ratio); Abundance (ecology); Cartography; Ecology; Remote sensing; Physical geography; Environmental science; Habitat; 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":[0.001535471,0.0003277042,0.0003125863,0.0009652592,0.00107435,0.00083306,0.0009164041,0.0003529645,0.001187381],"category_scores_gemma":[0.003959384,0.0003442575,0.0002154581,0.001272035,0.0007015429,0.0005185031,0.0006022327,0.0004222113,0.0001651127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001968408,"about_ca_system_score_gemma":0.002451096,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8548457,"about_ca_topic_score_gemma":0.9653168,"domain_scores_codex":[0.9993711,0.0001067561,0.00001201151,0.0001463803,0.000188383,0.0001753795],"domain_scores_gemma":[0.9976732,0.0006185807,0.0003796953,0.0002087189,0.0009100517,0.0002096437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001329976,0.00004444659,0.9488246,0.00004892726,0.0001914586,0.0000413304,0.001155679,0.001049918,0.002681678,0.00007901051,0.001340426,0.04440951],"study_design_scores_gemma":[0.000003411725,0.00001476922,0.9976116,0.00000989755,0.0000392101,0.0000251958,0.0004373624,0.001093644,0.0001200178,0.00003300316,0.0006045816,0.000007194875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946637,0.0005214413,0.000766873,0.0001641195,0.00001343248,0.00003026438,0.0005431012,0.00002830145,0.003268717],"genre_scores_gemma":[0.997398,0.0003679554,0.001150528,0.00007269716,0.00001284845,0.00001553242,0.0003673491,0.00001124487,0.0006039416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1451543,"threshold_uncertainty_score":0.2920182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888081194501232,"score_gpt":0.2691273245714544,"score_spread":0.2402465126264421,"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."}}