{"id":"W2056969597","doi":"10.1371/journal.pone.0099546","title":"Calibrating Snakehead Diversity with DNA Barcodes: Expanding Taxonomic Coverage to Enable Identification of Potential and Established Invasive Species","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Academy of Natural Sciences of Drexel University; Universiti Sains Malaysia; Drexel University; Virginia Department of Game and Inland Fisheries; Florida Museum of Natural History; Ontario Ministry of Agriculture, Food and Rural Affairs; New York State Department of Environmental Conservation","keywords":"DNA barcoding; Biology; Species complex; Environmental DNA; Biodiversity; Barcode; Genetic diversity; Evolutionary biology; Zoology; Ecology; Phylogenetic tree; Genetics; Population","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":[0.002305822,0.000483892,0.0004441466,0.005167611,0.000587096,0.001111483,0.0006174772,0.0006364923,0.0009184631],"category_scores_gemma":[0.006042259,0.0004106039,0.0003829355,0.00260338,0.0007501707,0.001555343,0.001403692,0.000690527,0.0004772898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004493692,"about_ca_system_score_gemma":0.0004807805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004008999,"about_ca_topic_score_gemma":0.008115577,"domain_scores_codex":[0.9981613,0.0003604299,0.0001653089,0.0006468673,0.0005304129,0.0001357109],"domain_scores_gemma":[0.9961275,0.0009892033,0.001413568,0.0003024864,0.0009667075,0.0002004531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001849821,0.0001076742,0.7455921,0.0003818526,0.0001840114,0.0001603395,0.0032694,0.002656645,0.1415704,0.0006329793,0.0004695857,0.1047901],"study_design_scores_gemma":[0.0000118761,0.0002425298,0.9483081,0.0001580885,0.0001365904,0.000509241,0.001715181,0.01988263,0.02346429,0.0009061913,0.004596982,0.00006837992],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593571,0.0005104364,0.03654549,0.000062527,0.0000183554,0.000134603,0.0009831907,0.0002599517,0.002128447],"genre_scores_gemma":[0.9238485,0.0004076358,0.07273085,0.0001134873,0.00001915963,0.0001590808,0.002137716,0.00005665205,0.0005269058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005167611,"threshold_uncertainty_score":0.01219451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933355976264986,"score_gpt":0.2097990019519394,"score_spread":0.1804654421892895,"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."}}