{"id":"W2587681257","doi":"10.1002/jwmg.21223","title":"Predictable features attract urban coyotes to residential yards","year":2017,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; Canadian Wildlife Federation; Alberta Conservation Association","keywords":"Wildlife; Geography; Foraging; Canis; Yard; Habitat; Human–wildlife conflict; Ecology; Wildlife disease; Biology","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":[0.0001621466,0.0001805888,0.0001979044,0.0005541792,0.0003680589,0.0004492358,0.000222598,0.0002753027,0.002460894],"category_scores_gemma":[0.0007443971,0.0001774874,0.0001114797,0.0002612815,0.0003397856,0.000175261,0.0005672227,0.0002234388,0.0003738364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921846,"about_ca_system_score_gemma":0.0001018838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003612041,"about_ca_topic_score_gemma":0.02007137,"domain_scores_codex":[0.9998415,0.00003288814,0.000007779389,0.00005693067,0.0000221934,0.00003880103],"domain_scores_gemma":[0.9991793,0.0001449277,0.0003645599,0.00005842824,0.0000917416,0.0001610128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006788181,0.00005646426,0.9931303,0.00001183668,0.00001797653,0.0000903913,0.0002285928,0.0001027907,0.004007006,0.00002690482,0.0001431544,0.002116749],"study_design_scores_gemma":[0.000001330778,0.00004468431,0.9990975,0.000004375345,0.000004074744,0.00007477464,0.00030904,0.0002221694,0.0001187688,0.000009638836,0.0001120125,0.00000167675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995721,0.00001617476,0.00005243097,0.000006172304,0.000001009919,0.00000293394,0.00002784898,0.000001899091,0.0003194481],"genre_scores_gemma":[0.9994888,0.00001725327,0.0001687471,0.000008000481,0.000002368913,0.000004969265,0.00007385464,0.000001804182,0.0002340901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003612041,"threshold_uncertainty_score":0.008232534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109250329867623,"score_gpt":0.248361623600315,"score_spread":0.2372691203016388,"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."}}