{"id":"W2999039702","doi":"","title":"Necessity of Quality-Controlled 16S rRNA Gene Sequence Databases: Identifying Nontuberculous Species.","year":2002,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Health Canada","funders":"","keywords":"Gene sequence; 16S ribosomal RNA; Biology; Sequence (biology); Gene; Sequence database; Database; Ribosomal RNA; Genetics; Computational biology; Microbiology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01482906,0.0006222125,0.001329138,0.002972763,0.0008050296,0.00267818,0.002139667,0.00134007,0.001708482],"category_scores_gemma":[0.04282182,0.0007219349,0.0005650158,0.002429929,0.0006811364,0.002937787,0.001141783,0.001607606,0.001998513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004328124,"about_ca_system_score_gemma":0.003065499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009522553,"about_ca_topic_score_gemma":0.002308271,"domain_scores_codex":[0.9915644,0.003019222,0.001374243,0.0009009815,0.002863046,0.0002781109],"domain_scores_gemma":[0.9465132,0.02756059,0.005032314,0.006733973,0.01221566,0.00194423],"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":[0.002266099,0.0008285285,0.02693925,0.005253356,0.0003290819,0.0009018629,0.0004953097,0.001747325,0.688536,0.0033056,0.008399491,0.2609982],"study_design_scores_gemma":[0.0006218037,0.002037714,0.05745807,0.002526749,0.00125472,0.00962839,0.00249942,0.03341787,0.7621571,0.01366087,0.1144661,0.0002711979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5001784,0.0325918,0.4212183,0.00930638,0.00114553,0.00193613,0.02388401,0.004381007,0.005358465],"genre_scores_gemma":[0.3073044,0.00857644,0.6270701,0.001881443,0.0003336873,0.0008829719,0.05256765,0.0005262762,0.0008570128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01482906,"threshold_uncertainty_score":0.07842457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461478972711771,"score_gpt":0.428475727486291,"score_spread":0.2823278302151139,"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."}}