{"id":"W3136377059","doi":"10.1111/eva.13232","title":"Varying genetic imprints of road networks and human density in North American mammal populations","year":2021,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Biology; Ecology; Mammal; Genetic diversity; Habitat fragmentation; Wildlife; Habitat; Population; Loss of heterozygosity; Range (aeronautics); Population fragmentation; Biological dispersal; Effective population size; Evolutionary biology; Genetic variation; Allele; Gene flow; Demography; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0005360566,0.0001386027,0.0002259903,0.0007848305,0.0002720039,0.0003449852,0.0002458554,0.0001879949,0.0007211624],"category_scores_gemma":[0.001638899,0.00020182,0.0002410822,0.0006712106,0.0007034276,0.0002994224,0.0006162041,0.0002223415,0.0000627553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002413129,"about_ca_system_score_gemma":0.0001196226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007732929,"about_ca_topic_score_gemma":0.02417621,"domain_scores_codex":[0.9994641,0.0002342036,0.0000210312,0.0001705286,0.00006080171,0.00004935254],"domain_scores_gemma":[0.9988561,0.0003961073,0.0004065675,0.0001441008,0.0001049865,0.00009219089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007868813,0.00002709986,0.9889566,0.000009347473,0.0001243454,0.00006154276,0.0007528303,0.0004067832,0.004347729,0.00006700017,0.00004663171,0.005121292],"study_design_scores_gemma":[4.149026e-7,0.00000848724,0.9996387,8.918627e-7,0.000007149966,0.00001694418,0.00008844607,0.0001706035,0.00002593828,0.0000223861,0.00001861501,0.000001502644],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997823,0.00002438007,0.00005569993,0.000004391387,2.958082e-7,7.228909e-7,0.00002390191,0.000001623625,0.0001068224],"genre_scores_gemma":[0.9997681,0.00001991923,0.00009223936,0.000005547132,0.000001219534,0.000003173591,0.00006721605,0.000001239041,0.00004139666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007732929,"threshold_uncertainty_score":0.01537585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162894688969874,"score_gpt":0.2429283396336172,"score_spread":0.2312993927439185,"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."}}