{"id":"W2072731863","doi":"10.1371/journal.pone.0035858","title":"A Ranking System for Reference Libraries of DNA Barcodes: Application to Marine Fish Species from Portugal","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundação para a Ciência e a Tecnologia; Programa Operacional Temático Factores de Competitividade; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; European Commission; Genome Canada; Ontario Genomics; Ontario Genomics Institute; Alfred P. Sloan Foundation","keywords":"Barcode; DNA barcoding; Biology; Ranking (information retrieval); Cytochrome c oxidase subunit I; Mitochondrial DNA; Phylogenetic tree; Computational biology; Evolutionary biology; Computer science; Information retrieval; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001302693,0.00007560584,0.0001308779,0.00003684259,0.0000550158,0.00002409208,0.0001619873,0.00007247459,0.00003773238],"category_scores_gemma":[0.00008966016,0.00008211546,0.0000349913,0.00007083653,0.00003115968,0.000009978648,0.00006673237,0.00002773296,0.00001789408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009377962,"about_ca_system_score_gemma":0.00002231085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001181792,"about_ca_topic_score_gemma":0.00001091217,"domain_scores_codex":[0.9992782,0.00002194224,0.0002473773,0.0001939159,0.0001300148,0.0001285267],"domain_scores_gemma":[0.9992638,0.00002254645,0.0001309559,0.000365061,0.0001559809,0.00006161076],"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":[0.00006831209,0.0002427801,0.004696639,0.0001178872,0.00007763829,1.305468e-8,0.0001029152,7.297613e-7,0.9849747,0.008639401,0.000584877,0.0004941148],"study_design_scores_gemma":[0.0001837468,0.00004007679,0.01747968,0.00003738947,0.0000465245,2.516428e-7,0.0001736739,0.00005499891,0.9738949,0.00004543984,0.007943309,0.00009998798],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760051,0.00007512694,0.02061722,0.000357281,0.00006921242,0.0005276865,0.000324937,0.00003180273,0.001991654],"genre_scores_gemma":[0.9901922,0.0000188306,0.00693831,0.00008622091,0.0002488138,0.0002026336,0.001381856,0.00001455734,0.0009166023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0141871,"threshold_uncertainty_score":0.3348571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06352994826525458,"score_gpt":0.2448626712031237,"score_spread":0.1813327229378691,"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."}}