{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008865795,0.0001195835,0.0001524457,0.0002060415,0.0000980124,0.000032656,0.00005203951,0.0001225178,0.00008703693],"category_scores_gemma":[0.000006980397,0.0001155976,0.0001047029,0.0002326857,0.0001016064,0.00000410805,0.00002284846,0.00008102499,0.00001619793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001895182,"about_ca_system_score_gemma":0.00007024247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005317094,"about_ca_topic_score_gemma":0.0001015899,"domain_scores_codex":[0.9991488,0.00007784466,0.0002286212,0.0003429787,0.00003728345,0.0001645093],"domain_scores_gemma":[0.9997419,0.00003395039,0.00001405801,0.00007681669,0.00007793431,0.00005541092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004213467,0.00009561641,0.002630838,0.0001274587,0.001150245,0.000002532785,0.0000958532,0.0005641955,0.9911664,0.002286101,0.0001651476,0.001673535],"study_design_scores_gemma":[0.008923737,0.006842573,0.2896934,0.000608569,0.01401765,0.0009554247,0.001209914,0.4829124,0.0775058,0.0218671,0.0902835,0.005180028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6217982,0.0009065217,0.3761938,0.0001094445,0.0001092028,0.00008439878,0.0007667696,0.00001675813,0.00001484389],"genre_scores_gemma":[0.9876817,0.00006002773,0.008202032,0.0001309904,0.00005492342,0.000006663377,0.003692643,0.00001380415,0.0001571556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9136605,"threshold_uncertainty_score":0.4713932,"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."}}