{"id":"W755741475","doi":"10.1371/journal.pone.0129606","title":"Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":193,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Princess Margaret Cancer Centre; Hospital for Sick Children; University of Toronto; Public Health Ontario","funders":"Canadian Institutes of Health Research; Crohn's and Colitis Canada; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Akaike information criterion; Covariate; Model selection; Goodness of fit; Statistics; Count data; Type I and type II errors; Parametric statistics; Mathematics; Selection (genetic algorithm); Information Criteria; Likelihood-ratio test; Parametric model; Computer science; Econometrics; Artificial intelligence; Poisson distribution","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":[0.1110599,0.002340226,0.003181866,0.003077148,0.001923255,0.003902287,0.00508069,0.003870172,0.002638625],"category_scores_gemma":[0.2303699,0.001233586,0.006750982,0.001918175,0.002884867,0.003852691,0.0036033,0.004529801,0.0004595534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002118056,"about_ca_system_score_gemma":0.003166384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004870361,"about_ca_topic_score_gemma":0.003909159,"domain_scores_codex":[0.9419665,0.04973864,0.001596937,0.003967417,0.001823194,0.0009073747],"domain_scores_gemma":[0.6367075,0.335067,0.008999533,0.01163887,0.005586232,0.002000851],"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":[0.004483071,0.000637671,0.1069209,0.001505429,0.004256739,0.001934645,0.003012169,0.7226316,0.003326074,0.07850808,0.003406729,0.06937691],"study_design_scores_gemma":[0.0003649351,0.0007380526,0.006731639,0.0001805964,0.0005193956,0.000350967,0.0003882069,0.9386479,0.001368338,0.04925737,0.001288563,0.0001640173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3189636,0.001329595,0.6747984,0.001357632,0.0001520519,0.0008246386,0.001046564,0.0005902719,0.0009372274],"genre_scores_gemma":[0.7472057,0.000469468,0.2456677,0.0007597672,0.00009532337,0.002003567,0.002543877,0.0002661964,0.0009884649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1110599,"threshold_uncertainty_score":0.5873478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219719700579063,"score_gpt":0.3207914810493371,"score_spread":0.1988195109914307,"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."}}