{"id":"W2009169025","doi":"10.1371/journal.pone.0006300","title":"Barcoding Nemo: DNA-Based Identifications for the Ornamental Fish Trade","year":2009,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"DNA barcoding; Barcode; Biology; Evolutionary biology; Species name; Ornamental plant; Taxon; Biodiversity; Identification (biology); Ecology; Zoology; Taxonomy (biology)","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":[0.002076991,0.0005800411,0.0004486133,0.00241415,0.0006672232,0.00100567,0.001223869,0.0006328195,0.005366586],"category_scores_gemma":[0.006073131,0.0002813557,0.0002601966,0.001823193,0.0008581197,0.001129752,0.001414245,0.0009635101,0.003072272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008591146,"about_ca_system_score_gemma":0.001562527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150744,"about_ca_topic_score_gemma":0.005402466,"domain_scores_codex":[0.9983399,0.0002713564,0.0001108075,0.0005285089,0.0006323489,0.00011715],"domain_scores_gemma":[0.9966124,0.0006290006,0.001488821,0.0003312941,0.0007385369,0.0002000065],"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":[0.0004965835,0.0002073516,0.1143038,0.001484448,0.0001113457,0.0002874624,0.0009060875,0.0009849116,0.497783,0.005196853,0.009070925,0.3691673],"study_design_scores_gemma":[0.00005818045,0.0006055712,0.4804718,0.001327817,0.0002363104,0.002056761,0.001635418,0.02420627,0.3301651,0.009818079,0.1491978,0.0002208629],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4642849,0.004989286,0.4666452,0.003203112,0.0005591899,0.001123018,0.03390088,0.004230758,0.02106369],"genre_scores_gemma":[0.5018524,0.001656621,0.4631757,0.0008886489,0.0001699917,0.000804817,0.0219221,0.0005292204,0.009000511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005366586,"threshold_uncertainty_score":0.01795304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817266651102181,"score_gpt":0.2763093886130923,"score_spread":0.2081367221020705,"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."}}