{"id":"W1893385597","doi":"10.1111/j.1558-5646.2012.01649.x","title":"PHENOTYPIC PLASTICITY FACILITATES MUTATIONAL VARIANCE, GENETIC VARIANCE, AND EVOLVABILITY ALONG THE MAJOR AXIS OF ENVIRONMENTAL VARIATION","year":2012,"lang":"en","type":"article","venue":"Evolution","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":235,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Erwin Schrödinger International Institute for Mathematics and Physics","keywords":"Evolvability; Biology; Phenotypic plasticity; Phenotype; Genetic variation; Evolutionary biology; Trait; Genetics; Selection (genetic algorithm); Quantitative genetics; Phenotypic trait; Genetic architecture; Variance (accounting); Population; Quantitative trait locus; Adaptation (eye); Gene","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":[0.0003479288,0.000329575,0.0002406596,0.0003916753,0.0002846846,0.0009366072,0.0004259309,0.0003972983,0.001261831],"category_scores_gemma":[0.002325666,0.0002158288,0.0003522807,0.0003236435,0.0009274213,0.001122648,0.0006119602,0.0004639863,0.0001392762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006018643,"about_ca_system_score_gemma":0.0003229614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001143779,"about_ca_topic_score_gemma":0.001279022,"domain_scores_codex":[0.9997116,0.00008722622,0.00001147892,0.00009897298,0.00005483423,0.00003592504],"domain_scores_gemma":[0.9992316,0.0003356904,0.0002110305,0.000118112,0.00004170003,0.00006180958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002253601,0.0001477161,0.04380516,0.0001159065,0.0001994178,0.0006990987,0.000441062,0.4301297,0.2214042,0.2614456,0.0003840717,0.04100266],"study_design_scores_gemma":[0.00004328605,0.000159801,0.06462859,0.00002356714,0.00009921343,0.0009254342,0.000146265,0.7270555,0.01464165,0.1899407,0.002254794,0.00008116251],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727764,0.0001325831,0.1197008,0.0003292128,0.00001113507,0.00001741432,0.0000789996,0.0001177751,0.006835581],"genre_scores_gemma":[0.9949555,0.00006976359,0.004262873,0.00001778889,0.000003893903,0.00001456297,0.00002216405,0.000008955015,0.0006445398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001261831,"threshold_uncertainty_score":0.004366815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00446669593659695,"score_gpt":0.2025205234542487,"score_spread":0.1980538275176517,"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."}}