{"id":"W2530201237","doi":"10.1371/journal.pone.0161449","title":"A DNA Barcode Library for North American Pyraustinae (Lepidoptera: Pyraloidea: Crambidae)","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Lepidoptera: Biology and Taxonomy","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Smithsonian Tropical Research Institute; Washington State University; National Natural Science Foundation of China; Ontario Genomics Institute; Natural Science Foundation of Shaanxi Province; Canada Research Chairs; Ontario Genomics; Genome Canada; Washington State Department of Agriculture; Ministry of Science and Technology of the People's Republic of China; College of Charleston; York University; Natural Sciences and Engineering Research Council of Canada; Smithsonian Institution","keywords":"DNA barcoding; Barcode; Biology; Intraspecific competition; Evolutionary biology; Species complex; Taxonomy (biology); Zoology; Phylogenetic tree; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005524771,0.0006629697,0.0006440851,0.002313631,0.0008556136,0.0007111304,0.0007398623,0.0006964948,0.003900073],"category_scores_gemma":[0.002292429,0.0003800945,0.0005331506,0.001875452,0.0004118805,0.0005766991,0.0007488864,0.0006698495,0.002127851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043408,"about_ca_system_score_gemma":0.001429863,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002856502,"about_ca_topic_score_gemma":0.01089354,"domain_scores_codex":[0.9989628,0.000107406,0.00009226231,0.0003988807,0.0003386141,0.0001001127],"domain_scores_gemma":[0.9991099,0.0001634385,0.0002510938,0.0001267805,0.0002668274,0.00008200335],"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":[0.0004922652,0.0002306974,0.01278337,0.001012602,0.00008726349,0.0004350354,0.0008597674,0.0004086885,0.8643781,0.000823736,0.001791141,0.1166973],"study_design_scores_gemma":[0.0002497801,0.003107825,0.4316023,0.0007175759,0.0008404145,0.005114256,0.001207114,0.01049948,0.3803383,0.0009244216,0.1651901,0.00020847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7495102,0.003497049,0.1807605,0.0003349259,0.0002517784,0.003516439,0.04940312,0.002781523,0.009944445],"genre_scores_gemma":[0.3778171,0.003115056,0.4623267,0.0005041728,0.000105174,0.002922273,0.1385811,0.0005093604,0.01411904],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.9971435,"threshold_uncertainty_score":0.0130471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307983797628199,"score_gpt":0.2130539069977044,"score_spread":0.1899740690214224,"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."}}