{"id":"W2767687964","doi":"10.1371/journal.pone.0186525","title":"Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Guelph; Trent University; Ministry of Natural Resources and Forestry","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Woodland caribou; Selection (genetic algorithm); Range (aeronautics); Ecology; Habitat; Woodland; Threatened species; Linear regression; National park; Geography; Environmental science; Biology; Statistics","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":[0.0003723101,0.0001310748,0.0001837162,0.0003647874,0.0005920497,0.0005494308,0.0003454718,0.0001825427,0.0005700945],"category_scores_gemma":[0.001066319,0.0001416286,0.0001733077,0.0005280864,0.0007466687,0.0002099876,0.0003131823,0.0001155249,0.00004426203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003074239,"about_ca_system_score_gemma":0.001898623,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7827228,"about_ca_topic_score_gemma":0.9527635,"domain_scores_codex":[0.9997784,0.00005211086,0.00001211652,0.00005600599,0.00003989683,0.00006141741],"domain_scores_gemma":[0.9993119,0.0001085481,0.0002344452,0.00004791336,0.0001387219,0.0001584539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004512933,0.00001030316,0.9947813,0.00001135372,0.00003174022,0.00007377105,0.0008450287,0.0001952142,0.001160096,0.00003199609,0.00007262249,0.002741399],"study_design_scores_gemma":[7.832202e-7,0.000005000022,0.9993219,0.000002067014,0.000004514985,0.00001397275,0.0004217932,0.0001186165,0.00002347425,0.000008347492,0.00007831531,0.00000117994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997048,0.00005141412,0.00002959151,0.00002332464,4.853172e-7,0.000001392836,0.00003237148,0.000001077235,0.000155525],"genre_scores_gemma":[0.9996958,0.00003909715,0.00005543456,0.00000674086,6.039204e-7,0.000002244239,0.00003161058,7.279045e-7,0.0001678197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2172772,"threshold_uncertainty_score":0.4371136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611768188650314,"score_gpt":0.2460007493721689,"score_spread":0.2198830674856657,"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."}}