{"id":"W3036120216","doi":"10.1002/cjs.11556","title":"Inference for misclassified multinomial data with covariates","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Multinomial distribution; Inference; Bayesian probability; Computer science; Classifier (UML); Subject (documents); Multinomial logistic regression; Statistics; Bayesian inference; Artificial intelligence; Econometrics; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.05286373,0.001504227,0.003385686,0.003088172,0.001208279,0.004226987,0.005205038,0.003259724,0.002984442],"category_scores_gemma":[0.2048826,0.001574082,0.00236786,0.003363545,0.003695399,0.005776869,0.003530133,0.00599215,0.0005121617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003041803,"about_ca_system_score_gemma":0.002601706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008576537,"about_ca_topic_score_gemma":0.006166782,"domain_scores_codex":[0.9746045,0.01557612,0.001674222,0.004328954,0.003039919,0.0007763542],"domain_scores_gemma":[0.8497319,0.1272184,0.009349817,0.009862788,0.002917042,0.0009201377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004846525,0.000181848,0.03566318,0.0006364759,0.001113195,0.001012736,0.001010605,0.3491814,0.0007484375,0.4995641,0.003571084,0.1068323],"study_design_scores_gemma":[0.00005195446,0.00004989488,0.002459882,0.0001468173,0.0001177204,0.0002092886,0.00009576532,0.5112012,0.0006797615,0.4834251,0.001525288,0.0000373031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02641646,0.0008624124,0.9706389,0.001009249,0.0001252922,0.00005433311,0.0002572693,0.0001586507,0.0004775351],"genre_scores_gemma":[0.5128446,0.002174193,0.4749008,0.001122387,0.0006650263,0.0003987238,0.002180298,0.0002192252,0.005494646],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05286373,"threshold_uncertainty_score":0.2795735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1936066752888718,"score_gpt":0.3686499324939307,"score_spread":0.1750432572050588,"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."}}