{"id":"W4223934158","doi":"10.21203/rs.3.rs-1499016/v1","title":"The Mini Colon Model: a benchtop multi-bioreactor system to investigate the gut microbiome","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences; Université du Québec à Montréal","keywords":"Bioreactor; Multiplexing; Metagenomics; Microbiome; Fermentation; Biology; Biochemical engineering; Computer science; Computational biology; Engineering; Bioinformatics; Food science; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005282213,0.000758972,0.0005100276,0.0003505267,0.0003329368,0.000652801,0.0005412019,0.0006009996,0.00139667],"category_scores_gemma":[0.0003230371,0.0002211938,0.0004881234,0.0002566883,0.0002486413,0.0004176246,0.0007196889,0.0006350974,0.0006421271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002289182,"about_ca_system_score_gemma":0.0004115849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006905988,"about_ca_topic_score_gemma":0.001362434,"domain_scores_codex":[0.9996194,0.00007903884,0.00002234857,0.000113418,0.0001183511,0.00004747084],"domain_scores_gemma":[0.9996551,0.00007643233,0.00007326588,0.00005525064,0.00005407442,0.00008585211],"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":[0.0001171602,0.00007622253,0.0008553462,0.00006874243,0.00001056985,0.00002767915,0.00001687643,0.0002125405,0.9959097,0.00004966512,0.0001411514,0.002514267],"study_design_scores_gemma":[0.00004081873,0.002800772,0.01586701,0.00003934794,0.00009609519,0.0006138712,0.00008868886,0.01296276,0.9594,0.0001465091,0.00786957,0.00007455805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8282603,0.002589482,0.1616046,0.0002880484,0.0002663047,0.0005812388,0.002823874,0.001612094,0.001974061],"genre_scores_gemma":[0.783319,0.00133021,0.2083539,0.000266572,0.0000851052,0.0009838488,0.002848068,0.00009990415,0.002713441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00139667,"threshold_uncertainty_score":0.004672289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07595758242626859,"score_gpt":0.4038614096208893,"score_spread":0.3279038271946207,"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."}}