{"id":"W3118804762","doi":"10.1007/s00357-023-09452-0","title":"Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data","year":2023,"lang":"en","type":"article","venue":"Journal of Classification","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Simons Foundation","keywords":"Cluster analysis; Microbiome; Multinomial logistic regression; Mixture model; Computer science; Multinomial distribution; Expectation–maximization algorithm; Human microbiome; Gaussian; Data mining; Pattern recognition (psychology); Artificial intelligence; Statistics; Mathematics; Machine learning; Bioinformatics; Biology; Maximum likelihood","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096145,0.00008160436,0.0001735656,0.000232282,0.00007605465,0.0001220374,0.001113236,0.00006027237,0.00000339647],"category_scores_gemma":[0.0002062859,0.00006791438,0.00007589762,0.0003174214,0.00002420707,0.0006486744,0.0001540177,0.0001119685,0.000008865377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004604909,"about_ca_system_score_gemma":0.00009782496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001990564,"about_ca_topic_score_gemma":0.000003923065,"domain_scores_codex":[0.9989972,0.00006856483,0.0004149828,0.0001964577,0.0001491945,0.0001736394],"domain_scores_gemma":[0.9986573,0.0001940457,0.0004234542,0.0004878583,0.0001610574,0.00007626737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009481213,0.00008196249,0.000380216,0.00009299022,0.00009558944,0.00001722959,0.0008402133,0.0003997173,0.3058987,0.006458143,0.008620711,0.6770197],"study_design_scores_gemma":[0.0006777032,0.00009322194,0.01482455,0.00002981907,0.00002701279,0.00003636134,0.00004034535,0.9727108,0.00183488,0.001625022,0.007954042,0.0001462033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00561377,0.00002784307,0.9921067,0.001246138,0.0007879135,0.00010087,0.00002733688,0.00003253856,0.00005683409],"genre_scores_gemma":[0.5222275,0.00002371421,0.4772895,0.00004845569,0.000279281,0.000001988579,0.00001779793,0.000007266451,0.0001045377],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9723111,"threshold_uncertainty_score":0.2769468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2580616931963199,"score_gpt":0.3938671886188428,"score_spread":0.135805495422523,"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."}}