{"id":"W4210867473","doi":"10.1002/qre.3078","title":"Analyzing count data with measurement error","year":2022,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Statistics; Observational error; Inference; Log-normal distribution; Count data; Statistical inference; Regression analysis; Mathematics; Population; Regression; Econometrics; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.006559904,0.0001098217,0.0001824593,0.0001005491,0.0002059971,0.0001165802,0.0009335653,0.00001949065,0.0001963941],"category_scores_gemma":[0.006936249,0.00008686227,0.00002313349,0.0002551037,0.00005309795,0.0003472765,0.0006984181,0.0002867242,0.000004727597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002423052,"about_ca_system_score_gemma":0.00005947928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005275937,"about_ca_topic_score_gemma":0.000007559994,"domain_scores_codex":[0.9962733,0.0001090158,0.0005180069,0.0005949449,0.002343434,0.0001612912],"domain_scores_gemma":[0.9978327,0.0008946903,0.0001315443,0.0006618053,0.0003898347,0.00008942962],"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":[0.000293605,0.0002932204,0.07935799,0.00008158203,0.0001229077,0.00001963509,0.0006119082,0.8679029,0.0006509567,0.02948567,0.0009092652,0.02027034],"study_design_scores_gemma":[0.0008958147,0.0001475355,0.185093,0.00004022716,0.00002757763,0.00003040246,0.001099999,0.6603325,0.00009662953,0.01955103,0.1320984,0.0005868757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08929152,0.000136902,0.9059949,0.002606159,0.001034153,0.0001397252,0.0003252613,0.00008394911,0.0003874608],"genre_scores_gemma":[0.9824613,0.000003228239,0.01725707,0.00004538897,0.00008780984,0.00002059476,0.00002450087,0.000008487566,0.00009156122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8931698,"threshold_uncertainty_score":0.830384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2238217057577922,"score_gpt":0.4341605126323592,"score_spread":0.2103388068745669,"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."}}