{"id":"W2020281045","doi":"10.1371/journal.pone.0049755","title":"The Chaperonin-60 Universal Target Is a Barcode for Bacteria That Enables De Novo Assembly of Metagenomic Sequence Data","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":159,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Saskatchewan Research Council (Canada); University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Barcode; Metagenomics; Biology; DNA barcoding; 16S ribosomal RNA; Computational biology; Phylum; Pyrosequencing; Genetics; Genome; Ribosomal RNA; Gene; Bacteria; Evolutionary 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000263487,0.0001165761,0.0001578414,0.00001290441,0.0001275139,0.00001576866,0.0004669002,0.00006350248,0.00001196229],"category_scores_gemma":[0.00006201911,0.00009298694,0.00004631538,0.0000258501,0.00009022326,0.000003116602,0.0003347674,0.00003593708,0.000002255039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001401057,"about_ca_system_score_gemma":0.00007711259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001979455,"about_ca_topic_score_gemma":0.00002007312,"domain_scores_codex":[0.9992163,0.00003473985,0.0001244894,0.0002146365,0.00007949816,0.0003303124],"domain_scores_gemma":[0.9991915,0.00004392035,0.00008689115,0.0005589954,0.00005733258,0.00006140762],"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":[0.0000533422,0.0001190198,0.003785816,0.00003105963,0.0004799844,1.846678e-7,0.0001660801,0.00000268129,0.9947872,0.00009828433,0.000340844,0.000135475],"study_design_scores_gemma":[0.0003048682,0.0001161773,0.00338097,0.00001063233,0.0001490232,0.000001838143,0.0001746034,0.0002001125,0.9714237,0.00005628113,0.02404453,0.0001372321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935564,0.004564921,0.0005610037,0.000246528,0.00004939675,0.0002238562,0.0006131235,0.000002275319,0.0001825015],"genre_scores_gemma":[0.9809917,0.001775722,0.01638313,0.0001295851,0.0001803306,0.00002109288,0.0001151561,0.00001983835,0.0003834375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02370369,"threshold_uncertainty_score":0.3791897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1069148353524106,"score_gpt":0.2666729967853607,"score_spread":0.1597581614329501,"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."}}