{"id":"W3094445727","doi":"10.1186/s12859-020-03815-9","title":"MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline","year":2020,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; University of Washington; National Institute on Aging; National Institutes of Health; Tel Aviv University; Israel Science Foundation","keywords":"Metagenomics; Computer science; Annotation; Pipeline (software); Personalization; Interoperability; Pipeline transport; Database; Data mining; World Wide Web; Operating system; Artificial intelligence; Engineering; Biology","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":[0.003297593,0.00388895,0.001758744,0.003246766,0.002394257,0.003349011,0.004577661,0.001892904,0.01289022],"category_scores_gemma":[0.004948641,0.0025127,0.004077584,0.001789504,0.001186409,0.003318763,0.005324924,0.004615764,0.02070118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353474,"about_ca_system_score_gemma":0.002666243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002439552,"about_ca_topic_score_gemma":0.002655992,"domain_scores_codex":[0.9977708,0.0002471014,0.0001843991,0.0008826911,0.0006388013,0.00027628],"domain_scores_gemma":[0.9982191,0.000465491,0.0002437778,0.0004086075,0.0004175086,0.0002454051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004566208,0.0005371825,0.01270861,0.004040776,0.001094389,0.002258806,0.00270584,0.01513825,0.3449341,0.01344134,0.3569452,0.2416293],"study_design_scores_gemma":[0.0006404857,0.0005063674,0.012428,0.0006446539,0.0004823527,0.001739752,0.000544841,0.149722,0.2987033,0.03864772,0.4949085,0.001032157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01441438,0.0007230134,0.4689305,0.0006171711,0.0003937322,0.0005537475,0.04001189,0.4681528,0.006202749],"genre_scores_gemma":[0.06977396,0.0006917124,0.6952737,0.001902288,0.0001694275,0.002501257,0.1519222,0.06810835,0.009657112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01289022,"threshold_uncertainty_score":0.04312205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03207415185522694,"score_gpt":0.2442036744170113,"score_spread":0.2121295225617843,"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."}}