{"id":"W2124881090","doi":"10.1093/czoolo/58.3.426","title":"A robust new metric of phenotypic distance to estimate and compare multiple trait differences among populations","year":2012,"lang":"en","type":"article","venue":"Current Zoology","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Trait; Biology; Metric (unit); Sample size determination; Population; Divergence (linguistics); Statistics; Phenotypic trait; Selection (genetic algorithm); Evolutionary biology; Mathematics; Genetics; Computer science; Phenotype; Artificial intelligence; Demography","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":[0.00004405744,0.00008899116,0.0001964444,0.00001561835,0.0001093787,0.00000932502,0.00009095603,0.00002350916,0.00006219857],"category_scores_gemma":[0.00006805042,0.00003493209,0.00002934525,0.0001839206,0.00006133341,0.00006246215,0.00006003217,0.00004933053,0.000005425073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005674547,"about_ca_system_score_gemma":0.000001589748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002817881,"about_ca_topic_score_gemma":0.01253501,"domain_scores_codex":[0.9994317,0.00002486793,0.0001363378,0.0001279207,0.00006244551,0.0002167469],"domain_scores_gemma":[0.9996679,0.0001290255,0.00005793247,0.00001351079,0.00001861301,0.0001129732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001083409,0.00004885749,0.9795761,0.000006342032,0.000004814227,8.405663e-8,0.0001380118,0.000004307605,0.001085119,0.0006472167,0.0002183591,0.01826003],"study_design_scores_gemma":[0.00006536623,0.00005242518,0.9983165,0.00001693716,0.00001375159,0.000001085566,0.0000692064,0.0001234516,0.00003696561,0.0001894649,0.001031408,0.00008348208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962513,0.003083037,0.0001227772,0.0001532859,0.0001442422,0.0001005664,0.00006529675,0.00002079639,0.00005873648],"genre_scores_gemma":[0.9995615,0.00003296892,0.0002179972,0.000007712619,0.0001138744,0.00000444851,0.00002292383,3.797537e-7,0.00003818037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01874043,"threshold_uncertainty_score":0.6994828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922169985642752,"score_gpt":0.2918791461151864,"score_spread":0.0996621475509113,"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."}}