{"id":"W2981525383","doi":"10.1111/1365-2656.13130","title":"Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species","year":2019,"lang":"en","type":"article","venue":"Journal of Animal Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cenovus Energy (Canada); Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"Cenovus Energy","keywords":"Predation; Predator; Habitat; Ecology; Apex predator; Woodland caribou; Population; Geography; Selection (genetic algorithm); Demographics; Biology; Demography; Computer science","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":[0.0006247307,0.000141456,0.0001763694,0.0004455986,0.0002146654,0.0009555384,0.0002154579,0.0004258947,0.002899782],"category_scores_gemma":[0.003789228,0.0001071054,0.0001761978,0.0006163997,0.0007504874,0.001082662,0.000282504,0.0003386766,0.0002939322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002725207,"about_ca_system_score_gemma":0.0002583545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01398807,"about_ca_topic_score_gemma":0.03974912,"domain_scores_codex":[0.999757,0.0001027794,0.00001425289,0.00005964727,0.00002902956,0.00003725664],"domain_scores_gemma":[0.9983473,0.0006576272,0.0006337507,0.00008272555,0.00009972623,0.0001789078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008436947,0.00001972406,0.9654788,0.0000540304,0.0001051245,0.00003768554,0.0005161998,0.0001816484,0.000237866,0.000769284,0.0009302964,0.031585],"study_design_scores_gemma":[0.000003934916,0.00003334106,0.9936522,0.00006630256,0.00003535674,0.0001680203,0.001867551,0.0005652616,0.00004931122,0.001509534,0.002038925,0.00001024756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885488,0.003803248,0.0008533333,0.002175507,0.00003548161,0.000007234745,0.0004237868,0.00001711698,0.004135636],"genre_scores_gemma":[0.9981346,0.000741006,0.0003388633,0.0001374687,0.00003299877,0.000004774442,0.00009268955,0.000004399446,0.0005131582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01398807,"threshold_uncertainty_score":0.02781332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009140025976990977,"score_gpt":0.2091965798439838,"score_spread":0.2000565538669928,"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."}}