{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037634,0.0002677934,0.0005068011,0.0007106572,0.0002854888,0.0004839512,0.0003876684,0.0003205194,0.0005137885],"category_scores_gemma":[0.01002536,0.000173314,0.000424171,0.0008552204,0.0005618442,0.0004942519,0.0005850173,0.000366046,0.0001200247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967987,"about_ca_system_score_gemma":0.0001712621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348089,"about_ca_topic_score_gemma":0.003059937,"domain_scores_codex":[0.9974279,0.001175525,0.0001497793,0.0008952838,0.0002608392,0.00009058871],"domain_scores_gemma":[0.9899023,0.005828349,0.001750031,0.002065713,0.0003082755,0.0001453981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000480811,0.00004716345,0.9671031,0.00004376043,0.0006521309,0.00004474456,0.0003192985,0.00522013,0.007122107,0.0004439362,0.0002428504,0.01871274],"study_design_scores_gemma":[0.000002930547,0.00007495624,0.9894056,0.000005380455,0.00006701531,0.00008968837,0.00009301484,0.007718773,0.001235762,0.0009259334,0.0003644093,0.00001655324],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804945,0.0001261322,0.01820307,0.00002012603,0.00000567868,0.00002160559,0.0004493279,0.00004964328,0.0006299351],"genre_scores_gemma":[0.9948243,0.00002264146,0.004630796,0.00001818587,0.000003222348,0.00002573038,0.0004000073,0.00001165845,0.00006344011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0037634,"threshold_uncertainty_score":0.01990294,"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."}}