{"id":"W1565238527","doi":"10.1111/j.1365-2656.2006.01106.x","title":"Application of random effects to the study of resource selection by animals","year":2006,"lang":"en","type":"article","venue":"Journal of Animal Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":819,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Conservation Association","keywords":"Selection (genetic algorithm); Pooling; Statistics; Resource (disambiguation); Categorical variable; Random effects model; Population; Ecology; Econometrics; Mathematics; Biology; Computer science; Demography; Machine learning; Artificial intelligence","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":[0.0006897569,0.00005877386,0.0002012282,0.00004241124,0.00006804847,0.000002832238,0.0001624958,0.00006266621,0.00006857803],"category_scores_gemma":[0.0000923959,0.00004265269,0.00004018222,0.0002186664,0.00005854329,0.00005859945,0.00003877574,0.000104759,0.00001325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000585659,"about_ca_system_score_gemma":0.00001160517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001896427,"about_ca_topic_score_gemma":0.0007605385,"domain_scores_codex":[0.999041,0.0002261539,0.0003996646,0.00009108554,0.0001406157,0.0001014497],"domain_scores_gemma":[0.9990938,0.0002861875,0.0004938677,0.00006899018,0.00003354518,0.00002365197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001026868,0.0004712846,0.8943238,0.000003952825,0.00002134793,0.000001166358,0.0001822183,0.001866585,0.08870412,0.00004005513,0.01278216,0.0005764728],"study_design_scores_gemma":[0.001131098,0.004881054,0.9877631,0.000002039905,0.00004265655,0.00001878162,0.0001255172,0.0002635706,0.003543439,0.0001064936,0.002086181,0.00003605225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978166,0.00001891726,0.0006076342,0.0008514837,0.00004042389,0.000358187,6.785514e-7,0.000002876985,0.0003032489],"genre_scores_gemma":[0.9995658,0.000001235955,0.0001380485,0.0001851492,0.00005413107,0.0000122281,4.135997e-7,0.000004046694,0.00003896315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09343935,"threshold_uncertainty_score":0.1739326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004010736863049636,"score_gpt":0.2186370680342837,"score_spread":0.214626331171234,"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."}}