{"id":"W3204723245","doi":"10.1101/2021.09.30.462621","title":"Evaluating live microbiota biobanking using an <i>ex vivo</i> microbiome assay and metaproteomics","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Clostridium difficile and Clostridium perfringens research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Government of Canada; Genome Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Ontario Genomics Institute","keywords":"Microbiome; Metaproteomics; Biology; Biobank; Fecal bacteriotherapy; Feces; Prebiotic; Glycerol; Gut flora; Microbiology; Metagenomics; Food science; Bioinformatics; Immunology; Biochemistry","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":[0.0009748603,0.001007179,0.0005411279,0.0006654607,0.0003636905,0.0008734223,0.0003478676,0.0005416085,0.001141601],"category_scores_gemma":[0.0005465155,0.0002062358,0.0004841494,0.0004652326,0.0003840948,0.0006665054,0.0006385941,0.0008485509,0.0005659099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002703771,"about_ca_system_score_gemma":0.0003422691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004673815,"about_ca_topic_score_gemma":0.000605767,"domain_scores_codex":[0.9994209,0.0001417275,0.00005380981,0.000151492,0.0001622444,0.00006975876],"domain_scores_gemma":[0.9996027,0.00006534631,0.0001393018,0.00006436849,0.00007704172,0.00005111355],"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":[0.00007985298,0.00003242596,0.001007982,0.00003612619,0.00001088645,0.00001621837,0.00001258909,0.00007394475,0.9968944,0.00003490455,0.00004486772,0.001755841],"study_design_scores_gemma":[0.000005563161,0.0002981556,0.01141987,0.00001674895,0.00003404016,0.000150188,0.0000574416,0.0007832192,0.9855319,0.0001106916,0.001578729,0.00001343253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8986114,0.003415095,0.0870386,0.0004899092,0.0002126904,0.0003131191,0.006779836,0.0006292419,0.002510165],"genre_scores_gemma":[0.8777665,0.002817347,0.1049283,0.0004793797,0.0001086075,0.0005057669,0.009217635,0.0003007054,0.003875778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001141601,"threshold_uncertainty_score":0.005155623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05699573916544853,"score_gpt":0.3151150243112495,"score_spread":0.258119285145801,"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."}}