{"id":"W2751386526","doi":"10.1002/ece3.3176","title":"Territory surveillance and prey management: Wolves keep track of space and time","year":2017,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst; Killam Trusts; Alberta Innovates - Technology Futures","keywords":"Predation; Canis; Geography; Geolocation; Cartography; Ecology; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002197229,0.00005458941,0.0000882707,0.00001613741,0.0003057141,0.000009187155,0.00005566701,0.0000877217,0.0001133354],"category_scores_gemma":[0.00002776751,0.00005507875,0.000007388413,0.00001242515,0.0005088803,0.0001986262,0.0001097591,0.00004609635,0.00002502968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001873771,"about_ca_system_score_gemma":0.000002491624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006145659,"about_ca_topic_score_gemma":0.0005661064,"domain_scores_codex":[0.99958,0.00004452087,0.00007843616,0.0001642451,0.00003147375,0.0001012853],"domain_scores_gemma":[0.9997314,0.00003832322,0.00008490623,0.0001146423,0.000002940485,0.00002783097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002436883,0.00001700278,0.9971634,0.00001090658,0.000009571527,0.000001334704,0.00006618984,0.000004893265,0.0002681854,0.0007095674,0.0008701194,0.0008545088],"study_design_scores_gemma":[0.0002755399,0.00006346055,0.9947386,0.000004142599,0.000009450563,0.000006949724,0.00002277706,0.0005783632,0.00001488265,0.003795689,0.0004344424,0.00005565149],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923756,0.0001056318,0.00002219597,0.0009057187,0.00008504991,0.000101323,0.000002047602,0.000009139329,0.006393306],"genre_scores_gemma":[0.9979915,0.000108427,0.000198154,0.00005549343,0.00001584813,0.000006840462,0.000001859237,0.000002292649,0.001619591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005615907,"threshold_uncertainty_score":0.2351337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004985339795284866,"score_gpt":0.1948555013734765,"score_spread":0.1898701615781917,"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."}}