{"id":"W4414083938","doi":"10.1038/s41564-025-02112-6","title":"Learning ecosystem-scale dynamics from microbiome data with MDSINE2","year":2025,"lang":"en","type":"article","venue":"Nature Microbiology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Defense Advanced Research Projects Agency; National Science Foundation; National Institutes of Health; Massachusetts Life Sciences Center; U.S. Department of Health and Human Services; National Institute of Diabetes and Digestive and Kidney Diseases; U.S. Department of Defense","keywords":"Microbiome; Benchmark (surveying); Inference; Bayesian probability; Dynamical systems theory; Noise (video); Bayesian inference; Time series; Limit (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002173608,0.0003124618,0.0003864138,0.0001244171,0.0001908048,0.00003659277,0.0008755145,0.0011151,0.00004247731],"category_scores_gemma":[0.00004338229,0.0002631509,0.00006492221,0.0002413618,0.0001280469,0.000006442438,0.0006151915,0.0008245547,0.00004313657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007207553,"about_ca_system_score_gemma":0.0002851742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009329414,"about_ca_topic_score_gemma":0.00465741,"domain_scores_codex":[0.9979762,0.0001744398,0.0003099354,0.0010129,0.00003238082,0.0004941631],"domain_scores_gemma":[0.9985264,0.00003628252,0.0001429753,0.001097277,0.0001290332,0.00006803149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001936458,0.00006129178,0.006749644,0.00005037925,0.0001673769,0.000005710963,0.00002003455,0.00001220559,0.9627228,0.00006894652,0.02898465,0.0009633351],"study_design_scores_gemma":[0.001724821,0.0003617643,0.002989526,0.0001314555,0.0001028513,0.0001690706,0.0001711335,0.0001093243,0.08830549,0.00002584333,0.9053994,0.0005092983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859787,0.00487383,0.001803316,0.001408495,0.0008400069,0.0003803348,0.002430387,0.00006255978,0.002222428],"genre_scores_gemma":[0.9614314,0.0002715522,0.003550212,0.002031962,0.0002628789,0.000008139048,0.02802292,0.00004229503,0.004378641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8764148,"threshold_uncertainty_score":0.9999821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003052808709757936,"score_gpt":0.2464192700439447,"score_spread":0.2433664613341867,"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."}}