{"id":"W3200683212","doi":"10.3390/e23091206","title":"Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre de Recherches Mathématiques","keywords":"Count data; Poisson distribution; Generalized linear model; Estimator; Quasi-likelihood; Zero-inflated model; Poisson regression; Statistics; Mathematics; Variance (accounting); Applied mathematics; Generalized estimating equation; Zero (linguistics); Overdispersion; Medicine; Population","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002329766,0.00007086491,0.0001038451,0.0000239966,0.00004233702,0.00005982593,0.0003632042,0.00002829342,0.000001866611],"category_scores_gemma":[0.00004668334,0.00006763971,0.00001780365,0.00009682772,0.000006638218,0.0002406375,0.0002387079,0.00004400582,0.000002423515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001731925,"about_ca_system_score_gemma":0.00007832221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005863982,"about_ca_topic_score_gemma":0.000008559975,"domain_scores_codex":[0.9991733,0.00004968439,0.0001149578,0.0004144149,0.00009873365,0.0001489197],"domain_scores_gemma":[0.9991937,0.00002500787,0.00003239661,0.000616384,0.00004017785,0.00009230855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002366253,0.00007279039,0.0001105784,0.00003303318,0.000006250297,0.000007785352,0.0002517351,0.0005010269,0.01197894,0.9607784,0.00119934,0.02503651],"study_design_scores_gemma":[0.0004332025,0.000004368419,0.0001615513,0.000005934679,0.000005821611,0.000001130798,0.000001088804,0.9251958,0.0009396211,0.0707035,0.002470773,0.00007717409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008917539,0.001370899,0.9868468,0.002488529,0.00005403538,0.0002239227,0.00005200494,0.00002493203,0.00002133305],"genre_scores_gemma":[0.2032557,0.0001479579,0.7953054,0.0006399178,0.00006850551,0.00006201532,0.00007657884,0.000008878576,0.0004350133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9246948,"threshold_uncertainty_score":0.2758267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213906726764632,"score_gpt":0.3148376654365138,"score_spread":0.2726985981688674,"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."}}