{"id":"W3133297500","doi":"10.1101/2021.02.24.432739","title":"Towns and Trails Drive Carnivore Connectivity using a Step Selection Approach","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Alberta Environment and Protected Areas; Parks Canada","funders":"University of Alberta; Parks Canada; University of Montana; National Science Foundation","keywords":"Ursus; Carnivore; Geography; Landscape connectivity; Grizzly Bears; Transect; Canis; Ecology; Physical geography; Cartography; Environmental resource management; Environmental science; Predation; Biological dispersal; Population; 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":[0.0008402374,0.0002190522,0.0002227487,0.001082771,0.0003627386,0.0007739711,0.0006936765,0.0004015909,0.002536964],"category_scores_gemma":[0.002682083,0.0002967197,0.0007325836,0.0006206204,0.0005435168,0.0003465812,0.0003991695,0.0002584917,0.0001461618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008459013,"about_ca_system_score_gemma":0.0006531221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06836579,"about_ca_topic_score_gemma":0.08227895,"domain_scores_codex":[0.9996949,0.0001514499,0.000009884178,0.00007932755,0.00002580389,0.00003856233],"domain_scores_gemma":[0.9984901,0.001022876,0.0001553699,0.00009009299,0.0001223633,0.0001192757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002206412,0.00009768998,0.565016,0.00003510954,0.000427482,0.0002239607,0.0002209132,0.4161479,0.001055605,0.003069781,0.0007128564,0.01277202],"study_design_scores_gemma":[0.00001583396,0.00006189196,0.1112447,0.00001022213,0.00006723033,0.00006904429,0.0001325107,0.8863925,0.0001966386,0.001491059,0.0003033796,0.00001500473],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888096,0.00003448308,0.009901891,0.00006734207,0.000004118149,0.0000196542,0.0003378782,0.00007494474,0.0007500062],"genre_scores_gemma":[0.9970438,0.00001563154,0.002344737,0.00000943507,0.000002191648,0.00001517079,0.0001917996,0.000006632801,0.0003707569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06836579,"threshold_uncertainty_score":0.1359357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509469242347658,"score_gpt":0.2048950026204519,"score_spread":0.1898003101969754,"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."}}