{"id":"W2118588449","doi":"10.1111/j.1558-5646.2010.01060.x","title":"ACROSS-ENVIRONMENT GENETIC CORRELATIONS AND THE FREQUENCY OF SELECTIVE ENVIRONMENTS SHAPE THE EVOLUTIONARY DYNAMICS OF GROWTH RATE IN IMPATIENS CAPENSIS","year":2010,"lang":"en","type":"article","venue":"Evolution","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; National Science Foundation","keywords":"Biology; Selection (genetic algorithm); Covariance; Replicate; Variance (accounting); Bayesian probability; Statistics; Natural selection; Evolutionary biology; Ecology; Computer science; Machine learning; Mathematics","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":[0.0002964018,0.0002809954,0.0001947602,0.0009379829,0.0003308276,0.000263155,0.0003959898,0.0002058635,0.0005825129],"category_scores_gemma":[0.0009586027,0.000268241,0.0001693463,0.0004360097,0.0005083939,0.0002626702,0.0005316064,0.0003507143,0.00006078504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008000307,"about_ca_system_score_gemma":0.0001804535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048625,"about_ca_topic_score_gemma":0.03332016,"domain_scores_codex":[0.9998273,0.00003288557,0.00001178612,0.00007492524,0.0000330067,0.00002011597],"domain_scores_gemma":[0.999511,0.0001624297,0.000164501,0.00007607257,0.00004559262,0.0000402891],"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":[0.0001906404,0.00009699129,0.4512021,0.00006011195,0.0001186179,0.000587828,0.001093322,0.003485881,0.5180759,0.0008537942,0.0001272139,0.02410749],"study_design_scores_gemma":[0.000003804633,0.00002820922,0.9952222,0.000004353036,0.00001214719,0.0001023244,0.00008393959,0.00252333,0.001736825,0.0001263335,0.0001477125,0.000008847716],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994658,0.00002678843,0.0002157662,0.00000920236,4.887865e-7,0.000001781381,0.00002677195,0.00000382016,0.0002496305],"genre_scores_gemma":[0.9993167,0.0000223933,0.0003984514,0.000008255013,9.684099e-7,0.000004968637,0.00006104785,0.000004931532,0.0001823851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01048625,"threshold_uncertainty_score":0.02085042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006342290196085232,"score_gpt":0.1940353271981596,"score_spread":0.1876930370020743,"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."}}