{"id":"W2178680814","doi":"10.1007/s00442-015-3500-6","title":"Quantifying consistent individual differences in habitat selection","year":2015,"lang":"en","type":"article","venue":"Oecologia","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Naturvårdsverket; Fonds Québécois de la Recherche sur la Nature et les Technologies; Narodowe Centrum Badań i Rozwoju; Austrian Science Fund; Norges Forskningsråd; Center for Advanced Study, University of Illinois at Urbana-Champaign; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Selection (genetic algorithm); Habitat; Ecology; Evolutionary biology; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002935262,0.00007217694,0.0001076471,0.00001312035,0.00007747845,0.00003622915,0.0000862466,0.00008474207,0.0001064843],"category_scores_gemma":[0.00008118572,0.00002549148,0.00003041068,0.0002113724,0.00004411485,0.00008111307,0.0000359029,0.0001025879,0.00005268347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003856266,"about_ca_system_score_gemma":0.000009706453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00016052,"about_ca_topic_score_gemma":0.003502231,"domain_scores_codex":[0.9992883,0.00006735512,0.0001327667,0.0002187183,0.000127171,0.0001656549],"domain_scores_gemma":[0.9997936,0.00004648211,0.00004562777,0.00001845645,0.00004373426,0.00005209409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001479602,0.00006987159,0.9640549,9.085987e-7,0.000002036162,0.000002166895,0.00005003324,0.000001397282,0.02639459,0.000072865,0.0003498245,0.008986633],"study_design_scores_gemma":[0.00008336549,0.0003692109,0.9972956,0.000003612187,0.000006016725,0.000008464924,0.0006769413,0.00001297377,0.0009085942,0.00008159121,0.0004710363,0.0000825958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986005,0.00005783862,7.824135e-7,0.0004415536,0.0002514081,0.0001124527,0.000002948248,0.0000561314,0.0004763391],"genre_scores_gemma":[0.9996352,0.000005948362,0.00005912292,0.00004320475,0.00009815377,0.00001484973,0.00001432199,3.34926e-7,0.0001288538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03324072,"threshold_uncertainty_score":0.1954327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.169721368048142,"score_gpt":0.2850984498420922,"score_spread":0.1153770817939502,"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."}}