{"id":"W3059861968","doi":"10.1101/2020.08.19.257501","title":"Accurate identification and quantification of commensal microbiota bound by host immunoglobulins","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research; Berlin Institute of Health; Deutsche Forschungsgemeinschaft; Kennedy Trust for Rheumatology Research; National Eye Institute; Volkswagen Foundation; Wellcome Trust; Linacre College, University of Oxford; Cleveland Clinic; Mayo Clinic","keywords":"Biology; Antibody; Computational biology; Microbiome; Identification (biology); Host (biology); Antibody Repertoire; Immunoglobulin heavy chain; Sorting; Immunology; Evolutionary biology; Genetics; Computer science; Ecology; Algorithm","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":[0.002129673,0.0007652474,0.000774678,0.001271562,0.0004458848,0.001361388,0.0006823965,0.0009775604,0.00178387],"category_scores_gemma":[0.003466984,0.000380298,0.0006205328,0.000871491,0.0006081641,0.0006792069,0.001312863,0.001015551,0.0008796804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005574267,"about_ca_system_score_gemma":0.0006704875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129652,"about_ca_topic_score_gemma":0.001545506,"domain_scores_codex":[0.9978818,0.0004287718,0.0001040647,0.0006801124,0.0007494061,0.0001558049],"domain_scores_gemma":[0.9980471,0.0007189238,0.0003773646,0.0002611263,0.0004628665,0.0001326466],"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":[0.0002969177,0.00007574127,0.02150279,0.000427918,0.0001255684,0.0000667776,0.0001020726,0.01198708,0.9324106,0.001625806,0.000773903,0.03060482],"study_design_scores_gemma":[0.00003063263,0.0005781113,0.06027906,0.0001679999,0.0001508874,0.000405545,0.0001941035,0.2075884,0.7149366,0.006249206,0.009295784,0.0001238053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5390636,0.00189667,0.4494711,0.0002577472,0.00009495152,0.0001173481,0.00500361,0.001615618,0.002479409],"genre_scores_gemma":[0.7820018,0.0009508785,0.2106412,0.0002333753,0.00006038848,0.0002371626,0.003695353,0.0003141025,0.001865755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002129673,"threshold_uncertainty_score":0.01126295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617418332768354,"score_gpt":0.2495344426307512,"score_spread":0.2333602593030677,"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."}}