{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001785867,0.0002423913,0.0002691563,0.00005528472,0.0001573497,0.00005741686,0.0002086507,0.00009264372,0.00004551914],"category_scores_gemma":[0.0001287738,0.0002286217,0.000144176,0.0002518359,0.00005638326,0.000003745857,0.0002485591,0.00008612109,0.0001489793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945416,"about_ca_system_score_gemma":0.0001118121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008395747,"about_ca_topic_score_gemma":0.00001476063,"domain_scores_codex":[0.9986975,0.00003204898,0.0005178601,0.000255423,0.0001960495,0.000301077],"domain_scores_gemma":[0.9991797,0.00002883142,0.0001811404,0.0002552,0.0001569836,0.0001982066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001152357,0.0003785134,0.02223151,0.0007747287,0.001305581,0.00000450996,0.00393587,0.351907,0.492833,0.001528637,0.0971389,0.02680932],"study_design_scores_gemma":[0.003625011,0.001261228,0.05597932,0.00002819948,0.0003614467,0.000043923,0.002074721,0.5750864,0.08473788,0.0001516039,0.2751465,0.001503758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2361355,0.0004654896,0.7606699,0.0004937252,0.0002623098,0.0004059583,0.000458372,0.00003009717,0.001078597],"genre_scores_gemma":[0.8931193,0.00004703256,0.1032878,0.001726912,0.0003937659,0.00001504088,0.001246747,0.00002527761,0.0001380833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6573821,"threshold_uncertainty_score":0.9322921,"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."}}