{"id":"W4399666123","doi":"10.1371/journal.pcbi.1012196","title":"mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics","year":2024,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Institute of General Medical Sciences; McMaster University; University of Wisconsin-Madison","keywords":"Interpretability; Replicate; Computer science; Markov chain; Microbiome; Parameterized complexity; Machine learning; Statistics; Mathematics; Algorithm; Biology; Bioinformatics","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":[0.002388369,0.001186573,0.001133213,0.001325262,0.0004370581,0.001226944,0.002239035,0.001437827,0.0235165],"category_scores_gemma":[0.01354027,0.0006665082,0.001729897,0.0007028587,0.0006142576,0.001347312,0.002020499,0.001696807,0.002861859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006295945,"about_ca_system_score_gemma":0.001711878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004810882,"about_ca_topic_score_gemma":0.004036672,"domain_scores_codex":[0.9995264,0.0002083899,0.00002872975,0.00009039245,0.0001002257,0.00004588405],"domain_scores_gemma":[0.9971557,0.002154687,0.0001884697,0.0002914665,0.0001230355,0.00008660513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001345699,0.0003673594,0.01444353,0.001313205,0.001132919,0.0008851149,0.0003598782,0.6057112,0.009328207,0.08930232,0.06921942,0.2065912],"study_design_scores_gemma":[0.00007819477,0.00008606554,0.0005995036,0.00002992512,0.00003747535,0.0000845525,0.0000212954,0.9663727,0.001678441,0.02518077,0.005808411,0.00002257751],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0172248,0.0002623252,0.9471399,0.0003828731,0.0001124192,0.0001369029,0.004326276,0.02874852,0.001665979],"genre_scores_gemma":[0.3218871,0.0004113892,0.6553615,0.0004008649,0.0001340894,0.001432802,0.00572918,0.00903217,0.005610933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0235165,"threshold_uncertainty_score":0.07867056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101645142959068,"score_gpt":0.3022483045172117,"score_spread":0.281231853087621,"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."}}