{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002512328,0.00007006545,0.00009246163,0.000007385175,0.0001889549,0.000004310921,0.00008370468,0.00006595792,0.0000509333],"category_scores_gemma":[0.00004771971,0.00002350308,0.00004207576,0.0001414569,0.0004833721,0.00007031501,0.00004638624,0.0001527815,0.000003481496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000562914,"about_ca_system_score_gemma":0.000005184708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251164,"about_ca_topic_score_gemma":0.001122752,"domain_scores_codex":[0.999305,0.0000987144,0.0001974931,0.0001656298,0.0001195214,0.000113685],"domain_scores_gemma":[0.9996785,0.00009065634,0.0001307963,0.00006023081,0.00002236177,0.00001747027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003723744,0.00008735579,0.7572238,0.000001789962,0.000006634808,1.519138e-7,0.0003744821,0.00009015789,0.2379926,0.001593086,0.000008590032,0.002584125],"study_design_scores_gemma":[0.0001567656,0.00008157361,0.9935308,0.000002967213,0.00001701678,0.000004762066,0.0003805634,0.002917758,0.0003227428,0.002529813,0.000007753484,0.00004755121],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998852,0.0001520033,0.00003523204,0.0005261321,0.00007943935,0.0002750451,0.00003520758,0.000003888273,0.00004105323],"genre_scores_gemma":[0.9997616,0.00006746812,0.00005212708,0.000005389424,0.00003805733,0.0000177263,0.00002132678,7.228016e-7,0.00003551995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2376699,"threshold_uncertainty_score":0.1891394,"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."}}