{"id":"W6976895863","doi":"10.6084/m9.figshare.23618031.v1","title":"Factor augmented inverse regression and its application to microbiome data analysis","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Overdispersion; Multinomial logistic regression; Count data; Support vector machine; Inference; Estimator; Regression; Multinomial distribution; Microbiome","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007484463,0.00008087329,0.000146347,0.0001335305,0.0000484249,0.00002581183,0.0002186597,0.00004655419,0.02671995],"category_scores_gemma":[0.004274893,0.00006462061,0.00001892223,0.0008510176,0.000002240077,0.00005539702,0.0003827417,0.00005048134,0.001412912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001238627,"about_ca_system_score_gemma":0.00001040002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003587327,"about_ca_topic_score_gemma":0.0000121187,"domain_scores_codex":[0.9993199,0.00004050364,0.0001340101,0.0002801306,0.0001013664,0.0001241317],"domain_scores_gemma":[0.9988767,0.0004733628,0.00005644114,0.0004397508,0.00005958649,0.00009416042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001212703,0.00004047773,0.0001156441,0.0005070879,0.0001571579,0.000007954525,0.0003085245,0.000002798531,0.01750863,0.0008369762,0.9400822,0.04042045],"study_design_scores_gemma":[0.001284321,0.0002067128,0.07508414,0.00425643,0.000779258,0.000006636973,0.0003908728,0.4905281,0.0267499,0.02824186,0.3707056,0.001766183],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01187572,0.00003738256,0.006147906,0.0002707139,0.00002668999,0.0005562336,0.9805635,0.000233989,0.0002878636],"genre_scores_gemma":[0.1852906,0.00002887807,0.1777329,0.0007699491,0.0002112026,0.0006690316,0.6313879,0.00009833365,0.003811172],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5693766,"threshold_uncertainty_score":0.9993646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.327611240104875,"score_gpt":0.4491323996006081,"score_spread":0.1215211594957331,"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."}}