{"id":"W4380551352","doi":"10.48550/arxiv.2306.06364","title":"Microbiome Intervention Analysis with Transfer Functions and Mirror Statistics","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Graduate Education; McMaster University","keywords":"Microbiome; Computer science; Inference; Psychological intervention; Function (biology); Intervention (counseling); Statistical inference; Data science; Machine learning; Artificial intelligence; Psychology; Biology; Statistics; Bioinformatics; Mathematics; Evolutionary biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01294017,0.001241041,0.001655751,0.001949763,0.0006776546,0.001618077,0.002707268,0.002119687,0.00694996],"category_scores_gemma":[0.0555223,0.000588519,0.002420292,0.001139182,0.002716056,0.002689503,0.003467371,0.002685115,0.0008545646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518096,"about_ca_system_score_gemma":0.00281404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003307771,"about_ca_topic_score_gemma":0.002068899,"domain_scores_codex":[0.9944957,0.003955958,0.0001560725,0.0006392903,0.0004797905,0.0002732062],"domain_scores_gemma":[0.9714898,0.02330622,0.001369913,0.002640394,0.0007888467,0.0004046982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005908386,0.0002023136,0.01121837,0.0003331251,0.0004516786,0.0004473579,0.0002285783,0.5921205,0.002520057,0.3030016,0.003606903,0.08527862],"study_design_scores_gemma":[0.00004589125,0.0001085445,0.0005163175,0.00002245771,0.00003188528,0.00004328531,0.00002071397,0.8586521,0.0008084128,0.1388048,0.0009291087,0.00001653231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0176057,0.0002062458,0.9798982,0.0003715514,0.00003292797,0.0001102379,0.0002763024,0.0006246329,0.0008743949],"genre_scores_gemma":[0.5934781,0.0004206471,0.398806,0.0005220736,0.000110236,0.001301212,0.0007628897,0.0005139046,0.004084919],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01294017,"threshold_uncertainty_score":0.06843495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05032686589441757,"score_gpt":0.2054113569843528,"score_spread":0.1550844910899352,"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."}}