{"id":"W2787925891","doi":"10.7717/peerj.5364","title":"Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches","year":2018,"lang":"en","type":"article","venue":"PeerJ","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":375,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Research Institute","keywords":"UniFrac; Spurious relationship; Pipeline (software); Amplicon; Cluster analysis; Operational taxonomic unit; Biology; Artificial intelligence; Computer science; False positive paradox; Metagenomics; Pattern recognition (psychology); Noise reduction; Computational biology; Data mining; Machine learning; 16S ribosomal RNA; Genetics; Gene; Polymerase chain reaction","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":[0.0006940418,0.0000895542,0.00007926612,0.00002476431,0.0001302944,0.00001711413,0.0001621065,0.00006609488,0.00001008944],"category_scores_gemma":[0.00005848478,0.00006938489,0.00004242935,0.00006754611,0.0001510289,0.000001415633,0.00007458289,0.00003846966,0.000004515129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000204548,"about_ca_system_score_gemma":0.00007042116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008124962,"about_ca_topic_score_gemma":0.0003010857,"domain_scores_codex":[0.9992425,0.0001011044,0.0001357594,0.0002268243,0.0001671086,0.000126689],"domain_scores_gemma":[0.9993307,0.00000691079,0.00008718051,0.0002902783,0.0002601198,0.00002484091],"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":[0.00002158097,0.00002848822,0.002891225,0.00000386692,0.00004199155,5.83996e-8,0.00053846,0.0003366505,0.9857708,0.00001561998,0.0002038392,0.01014741],"study_design_scores_gemma":[0.0003531551,0.0003575845,0.05345882,0.000008743451,0.0001070163,0.00002158796,0.001173432,0.003079416,0.9388965,0.0003218451,0.002055026,0.0001668571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974818,0.0007414017,0.0004544301,0.00008584587,0.0003712445,0.0001823847,0.000007652759,0.000002439055,0.0006728036],"genre_scores_gemma":[0.9991525,0.00002225607,0.0003278509,0.00005078427,0.0002839861,0.00001653689,0.00002646481,0.00001027529,0.0001093642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0505676,"threshold_uncertainty_score":0.2829433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1224366225942445,"score_gpt":0.3201658803552965,"score_spread":0.1977292577610521,"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."}}