{"id":"W4416711782","doi":"10.1139/cjm-2025-0075","title":"Deciphering the interrelation of gut microbiota and BMI in atherosclerosis: a metagenomic approach","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Microbiology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Biotechnology, Ministry of Science and Technology, India","keywords":"Metagenomics; Gut flora; Dysbiosis; Gut microbiome; Atherosclerotic cardiovascular disease; Obesity; Microbiome; Gut bacteria","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034394,0.0006798925,0.001425899,0.002674692,0.0005164783,0.001513393,0.0003513331,0.0004688878,0.0008578235],"category_scores_gemma":[0.001861732,0.0003264694,0.001761816,0.002155724,0.0002152782,0.0006544227,0.0008393907,0.001023274,0.0001698222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003676781,"about_ca_system_score_gemma":0.0007913803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003647591,"about_ca_topic_score_gemma":0.006227628,"domain_scores_codex":[0.9993767,0.0002948166,0.00003739543,0.0001509012,0.00008297608,0.00005728722],"domain_scores_gemma":[0.9994214,0.0002663197,0.00009045634,0.0000856265,0.00007919262,0.0000571053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001840957,0.0005442545,0.5630908,0.00279385,0.0236063,0.0005148939,0.00120836,0.01375721,0.2304463,0.002213498,0.00142739,0.1585563],"study_design_scores_gemma":[0.00006752793,0.001146353,0.8813043,0.0005840911,0.009779851,0.0005465956,0.001685011,0.07141126,0.013763,0.007701195,0.01185375,0.0001569468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8771633,0.03185206,0.08125594,0.001259934,0.000179083,0.0002776062,0.005722659,0.0003838801,0.001905511],"genre_scores_gemma":[0.935554,0.005976915,0.05396737,0.0003631547,0.00007697461,0.000137243,0.003208834,0.0000907628,0.0006248495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003647591,"threshold_uncertainty_score":0.01075906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00910517927021507,"score_gpt":0.2233900612539407,"score_spread":0.2142848819837257,"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."}}