{"id":"W1967862300","doi":"10.1371/journal.pone.0002490","title":"Identifying Canadian Freshwater Fishes through DNA Barcodes","year":2008,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":690,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of New Brunswick; Royal Ontario Museum; Ministry of Natural Resources and Wildlife; Fisheries and Oceans Canada; University of Guelph; Université Laval","funders":"Trent University; Idaho Department of Fish and Game; University of Manitoba; Ontario Genomics Institute; University of Windsor; Ontario Genomics; Genome Canada; Université Laval; Natural Sciences and Engineering Research Council of Canada; Massachusetts Department of Fish and Game","keywords":"DNA barcoding; Biology; Freshwater fish; Genetic distance; Species complex; Genetic divergence; Zoology; Context (archaeology); Mitochondrial DNA; Ecology; Fauna; Evolutionary biology; Genetic variation; Genetic diversity; Phylogenetic tree; Population; Fishery; Fish <Actinopterygii>; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007062473,0.00009363372,0.00009221153,0.00004760991,0.0002585982,0.00003866141,0.0001883397,0.00009412938,0.0003641912],"category_scores_gemma":[0.00006855606,0.00009902271,0.00004594199,0.00007808323,0.00008786047,0.000008981204,0.00002953187,0.00006096497,0.0003640565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001668104,"about_ca_system_score_gemma":0.00009012242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001839613,"about_ca_topic_score_gemma":0.01972271,"domain_scores_codex":[0.9991589,0.00003311519,0.0001731014,0.0002682559,0.0001540329,0.0002126408],"domain_scores_gemma":[0.9993467,0.000004690167,0.00004506808,0.0003790374,0.0001245366,0.00009994638],"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":[0.000006407982,0.0001754432,0.004803463,0.00002829536,0.0001068868,0.000003238869,0.0002989744,0.000001118235,0.9790184,0.0004008358,0.01512919,0.00002768965],"study_design_scores_gemma":[0.000141896,0.0000230793,0.004851073,0.00001953056,0.0000200555,0.000006726282,0.00006250063,0.00001466697,0.9519755,0.00006708812,0.0426675,0.0001503482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923987,0.0002896736,0.0002503968,0.0008478148,0.00008859822,0.0001355877,0.00005558863,0.00002506796,0.005908578],"genre_scores_gemma":[0.9859906,0.0003529349,0.002752124,0.0005823636,0.0001688386,0.00003352872,0.000362631,0.00002035147,0.009736642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02753831,"threshold_uncertainty_score":0.9981648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098369733370673,"score_gpt":0.2547361704059293,"score_spread":0.144899197068862,"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."}}