{"id":"W2602527715","doi":"10.1002/ecs2.1730","title":"Time vs. distance: Alternate metrics of animal resource selection provide opposing inference","year":2017,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; Carbon Engineering (Canada); University of Alberta","funders":"Parks Canada; World Wildlife Fund","keywords":"Woodland caribou; Habitat; Selection (genetic algorithm); Ursus; Foraging; Geography; Forage; Ecology; Endangered species; Metric (unit); Global Positioning System; Resource (disambiguation); Computer science; Biology; Population","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":[0.0224925,0.000907238,0.001190546,0.003483044,0.0006465692,0.00357567,0.001725301,0.001135447,0.00307059],"category_scores_gemma":[0.1154566,0.000415633,0.002086305,0.003201419,0.00255011,0.003669959,0.002372283,0.001737862,0.0005984751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006388108,"about_ca_system_score_gemma":0.0003809271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004535283,"about_ca_topic_score_gemma":0.003172732,"domain_scores_codex":[0.9749259,0.01779318,0.00136324,0.003284893,0.001934976,0.0006977185],"domain_scores_gemma":[0.7752259,0.2010478,0.01098109,0.008214703,0.003173458,0.001357016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001697329,0.0001291061,0.930417,0.0002907609,0.003203088,0.0002147665,0.0007556185,0.008666606,0.001541597,0.005814143,0.001189222,0.04608069],"study_design_scores_gemma":[0.0001512698,0.001135712,0.8004422,0.0002288755,0.001184541,0.0006816489,0.002732119,0.1520866,0.002266022,0.03530516,0.003611379,0.0001744978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9245087,0.00133589,0.06527782,0.0008320454,0.0002094572,0.00008853918,0.00123405,0.0001533121,0.006360232],"genre_scores_gemma":[0.9895354,0.0001422797,0.009110329,0.000148508,0.000106673,0.0000491827,0.0004429514,0.00004667961,0.0004180072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0224925,"threshold_uncertainty_score":0.1189531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130972445531433,"score_gpt":0.2446958173221088,"score_spread":0.2315985727689655,"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."}}