{"id":"W1965312634","doi":"10.1002/jwmg.435","title":"Keep in touch: Does spatial overlap correlate with contact rate frequency?","year":2012,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Home range; Range (aeronautics); Correlation; Intraspecific competition; Statistics; Proxy (statistics); Context (archaeology); Index (typography); Wildlife; Ecology; Demography; Biology; Mathematics; Materials science; Computer science; Habitat","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":[0.0007028711,0.0002141081,0.0003460379,0.001112697,0.0002780625,0.0008209305,0.0002914454,0.0004017065,0.002984771],"category_scores_gemma":[0.005907016,0.0002033752,0.0002608733,0.0009099529,0.0004787253,0.0005236788,0.0004332821,0.0003361154,0.0005337562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749017,"about_ca_system_score_gemma":0.0001031302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125738,"about_ca_topic_score_gemma":0.003148724,"domain_scores_codex":[0.9993374,0.0002021894,0.00007301509,0.0001814117,0.0001299419,0.00007604979],"domain_scores_gemma":[0.9924719,0.002538233,0.003519505,0.0003739079,0.0006046296,0.000491795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000658552,0.00002901244,0.9948109,0.00002111302,0.00008991532,0.00004745734,0.00009206551,0.0001592861,0.001605675,0.00002560862,0.00009937961,0.002953741],"study_design_scores_gemma":[6.195019e-7,0.00003600126,0.9990103,0.000003394078,0.00001242471,0.00009984719,0.0001110319,0.0005187712,0.0001224969,0.00003053033,0.00005199797,0.000002509282],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979141,0.0001949077,0.0006308486,0.00003314879,0.000005174076,0.000006089881,0.0001958875,0.00001325562,0.001006484],"genre_scores_gemma":[0.9993844,0.00004666977,0.0002361261,0.0000112554,0.000008113224,0.000006347213,0.0001477982,0.000004173941,0.0001550412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002984771,"threshold_uncertainty_score":0.00998503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006674031802234608,"score_gpt":0.2017733713487295,"score_spread":0.1950993395464949,"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."}}