{"id":"W3004254969","doi":"10.1111/jeb.13601","title":"Genetic variance for behavioural ‘predictability’ of stress response","year":2020,"lang":"en","type":"article","venue":"Journal of Evolutionary Biology","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Predictability; Trait; Biology; Quantitative genetics; Evolutionary biology; Genetic variation; Population; Selection (genetic algorithm); Genetic correlation; Natural selection; Genetics; Statistics; Gene; Demography; Machine learning; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002166355,0.00006751755,0.0001838292,0.00001077828,0.0000613604,0.000002704493,0.0001615287,0.00009106038,0.0001093745],"category_scores_gemma":[0.0002571516,0.00002640624,0.000145231,0.000121002,0.0001185012,0.00006173828,0.00002730264,0.00009409864,0.000001259972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001996172,"about_ca_system_score_gemma":0.00002686776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001827691,"about_ca_topic_score_gemma":0.000003104988,"domain_scores_codex":[0.9991476,0.0001401022,0.0003742947,0.0001400138,0.00008062848,0.000117373],"domain_scores_gemma":[0.9991884,0.0001714267,0.0002755958,0.00002856414,0.0002608021,0.00007527008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001695204,0.00008951063,0.3691447,0.000004937272,0.000008060333,0.000001986356,0.000038454,0.00001664777,0.6263836,0.00004653144,0.0004499751,0.00212034],"study_design_scores_gemma":[0.0001818499,0.003204096,0.9913225,0.000007800094,0.00002913328,0.00005563184,0.000125927,0.00003766302,0.002239374,0.0002635719,0.002476983,0.00005549542],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994876,0.0006117547,0.0001872384,0.003807173,0.0002308525,0.00011164,0.0001631462,0.000007207816,0.000004960656],"genre_scores_gemma":[0.9982047,0.00002546434,0.001165971,0.00005189192,0.000520341,0.000002739839,0.00001142473,5.726331e-7,0.00001689533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6241443,"threshold_uncertainty_score":0.1197574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04219953674983068,"score_gpt":0.2593386232494737,"score_spread":0.217139086499643,"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."}}