{"id":"W4390273482","doi":"10.1111/1462-2920.16566","title":"Mock microbial community meta‐analysis using different trimming of amplicon read lengths","year":2023,"lang":"en","type":"article","venue":"Environmental Microbiology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Alliance de recherche numérique du Canada; Ocean Frontier Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Trimming; Amplicon; Biology; Metagenomics; Trim; Amplicon sequencing; False positive paradox; Computational biology; Relative species abundance; Heuristics; Evolutionary biology; Abundance (ecology); Computer science; 16S ribosomal RNA; Genetics; Artificial intelligence; Ecology; Polymerase chain reaction; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002900411,0.0002751731,0.0006965648,0.0001889931,0.0002213691,0.000007437262,0.0003108873,0.0002806093,0.000257466],"category_scores_gemma":[0.00001036109,0.0002504048,0.0006451959,0.0001987386,0.0002828735,0.000003645963,0.0004373933,0.0002444706,0.00004277926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005006036,"about_ca_system_score_gemma":0.00002198509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001962122,"about_ca_topic_score_gemma":0.0001255062,"domain_scores_codex":[0.9982368,0.0004687735,0.0004282187,0.00039167,0.00003889485,0.0004356412],"domain_scores_gemma":[0.999086,0.00004719972,0.0002070079,0.0005774996,0.000007819206,0.00007449343],"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":[0.00005535463,0.0001666899,0.001870914,0.00002443733,0.009099158,0.000001417954,0.0001438916,0.0002233488,0.9881335,0.000004422134,0.0002204176,0.00005639641],"study_design_scores_gemma":[0.0008542201,0.0003975523,0.01684847,0.000004820022,0.01311018,0.0000594035,0.000519253,0.00002712213,0.9621581,0.00002324513,0.005549828,0.0004478144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978779,0.0008782322,0.0001603766,0.00005051641,0.0001055625,0.0002212564,0.0006342227,0.00001818893,0.00005376184],"genre_scores_gemma":[0.995928,0.0003031399,0.0002384857,0.0001566523,0.00004882224,0.0000094652,0.002600764,0.00002989716,0.0006847503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02597547,"threshold_uncertainty_score":0.9999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04889093585536401,"score_gpt":0.2862928708502343,"score_spread":0.2374019349948703,"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."}}