{"id":"W1920244167","doi":"10.1111/j.1365-294x.2005.02561.x","title":"How to use molecular marker data to measure evolutionary parameters in wild populations","year":2005,"lang":"en","type":"review","venue":"Molecular Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Biology; Selection (genetic algorithm); Heritability; Evolutionary biology; Quantitative genetics; Genetics; Genetic variation; Machine learning; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004858923,0.001406385,0.003312317,0.004853777,0.0003391747,0.001847319,0.00285214,0.00205756,0.001410426],"category_scores_gemma":[0.007411779,0.0007314243,0.0007509571,0.004152312,0.002020017,0.00485644,0.0007686733,0.002605059,0.002711562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095811,"about_ca_system_score_gemma":0.001010464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00179951,"about_ca_topic_score_gemma":0.001838068,"domain_scores_codex":[0.9987203,0.000300558,0.0001453667,0.0004038326,0.0003928837,0.00003709137],"domain_scores_gemma":[0.9947993,0.003522096,0.0003543257,0.0003090386,0.0009244694,0.00009072886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003901976,0.00003386489,0.001011237,0.008347471,0.0001324218,0.0001028382,0.00007687935,0.0009384705,0.002743531,0.008566973,0.006859583,0.9711477],"study_design_scores_gemma":[0.00003189728,0.0001356732,0.007168968,0.007215207,0.0003425724,0.001967483,0.0002805062,0.001199264,0.006700188,0.03374474,0.9410239,0.0001894447],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.0004507517,0.9866863,0.00959134,0.0009647718,0.0004089962,0.00001526803,0.000101567,0.00004332917,0.001737625],"genre_scores_gemma":[0.002386019,0.9796779,0.01592966,0.0005009039,0.0003660835,0.00003723192,0.0001258217,0.00001820995,0.0009581397],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004858923,"threshold_uncertainty_score":0.02569675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09169136677427321,"score_gpt":0.3141638911231883,"score_spread":0.2224725243489151,"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."}}