{"id":"W2044438617","doi":"10.1016/j.ympev.2012.07.033","title":"The effect of sampling from subdivided populations on species identification with DNA barcodes using a Bayesian statistical approach","year":2012,"lang":"en","type":"article","venue":"Molecular Phylogenetics and Evolution","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Ontario Genomics Institute; Genome Canada","keywords":"Biology; Range (aeronautics); Sampling (signal processing); Identification (biology); Bayesian probability; DNA barcoding; Evolutionary biology; Population; Sample (material); Subdivision; Phylogenetic tree; Statistics; Ecology; Genetics; Computer science; 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":[0.0003373353,0.0001426088,0.0001182476,0.00005082801,0.0002836854,0.00005834089,0.0001096928,0.0000867112,0.000002639452],"category_scores_gemma":[0.0001091348,0.0001082135,0.00004353255,0.0001154896,0.0001905365,0.000006073064,0.0000358272,0.00006080058,0.000001849019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002109487,"about_ca_system_score_gemma":0.00001970179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002509336,"about_ca_topic_score_gemma":0.000008530214,"domain_scores_codex":[0.9988719,0.0001892412,0.0002718125,0.000260239,0.0002073536,0.0001994869],"domain_scores_gemma":[0.9992455,0.00004320813,0.0001628107,0.0003870836,0.00008833179,0.00007304925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008607959,0.00004650708,0.02798181,0.00001674331,0.00004918588,6.306189e-8,0.00003897542,0.001075687,0.9586165,0.01163245,0.00002815963,0.0004278439],"study_design_scores_gemma":[0.0004038853,0.0002415108,0.331543,0.00001571537,0.000124403,0.000005700022,0.000101015,0.005088689,0.6615745,0.0002981333,0.0003987674,0.0002047223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6228218,0.0005407063,0.3762572,0.00001961711,0.00007503276,0.0001758894,0.00004455892,0.000004735537,0.00006042826],"genre_scores_gemma":[0.9941851,0.00003129302,0.005199242,0.00001047944,0.00008720782,0.00002766037,0.0004072828,0.00002074921,0.00003100936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3713633,"threshold_uncertainty_score":0.4412819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817328750714765,"score_gpt":0.2802337516487229,"score_spread":0.2520604641415753,"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."}}