{"id":"W4401414116","doi":"10.1101/2024.08.06.606757","title":"RiboSnake – a user-friendly, robust, reproducible, multipurpose and documentation-extensive pipeline for 16S rRNA gene microbiome analysis","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Workflow; Pipeline (software); Amplicon; Computer science; Amplicon sequencing; Documentation; Sample (material); Microbiome; Data mining; 16S ribosomal RNA; Pipeline transport; Computational biology; Data science; Bioinformatics; Database; Biology; Gene; Engineering; Polymerase chain reaction; Genetics; Operating system","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":[0.004830004,0.003051845,0.002162323,0.002243295,0.001237969,0.002663648,0.002548582,0.001194941,0.01854314],"category_scores_gemma":[0.006899116,0.002330108,0.002684772,0.001026536,0.001000997,0.001955436,0.003503084,0.003389672,0.02697904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006769387,"about_ca_system_score_gemma":0.002721309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126722,"about_ca_topic_score_gemma":0.001819306,"domain_scores_codex":[0.9969274,0.0005739175,0.0002906612,0.001043152,0.0008622002,0.0003026058],"domain_scores_gemma":[0.9978127,0.0007042647,0.0002733838,0.0005223172,0.0004434589,0.0002438844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003306312,0.0002597958,0.007082723,0.00520835,0.001041647,0.0009960759,0.001175635,0.0109766,0.3969343,0.007925333,0.3700572,0.195036],"study_design_scores_gemma":[0.0005756528,0.0003960156,0.01101946,0.0006495749,0.0002871025,0.001215796,0.0002119632,0.07427496,0.4208115,0.01943882,0.4701522,0.0009669591],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01232198,0.001105252,0.5561188,0.0006044259,0.0003350395,0.0007167768,0.03865063,0.3864683,0.003678654],"genre_scores_gemma":[0.04929259,0.0008871374,0.7738766,0.001058832,0.0001342162,0.002933084,0.1021023,0.0634252,0.006290042],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01854314,"threshold_uncertainty_score":0.06203306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112946954612683,"score_gpt":0.252928566915808,"score_spread":0.2417990973696811,"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."}}