{"id":"W4391619089","doi":"10.56801/jmasm.v23.i2.3","title":"The Performance of the Maximum Likelihood Estimator for the Bell Distribution for Count Data","year":2024,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Statistics; Count data; Estimator; Maximum likelihood; Econometrics; Poisson distribution","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":[0.008279569,0.0001617357,0.0003017045,0.00002249897,0.0004398668,0.0002478528,0.002269749,0.00007522224,0.000001813639],"category_scores_gemma":[0.0007695311,0.0000695542,0.0001322514,0.0001856607,0.0001871128,0.000173403,0.0003710074,0.0003788642,5.063545e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000545896,"about_ca_system_score_gemma":0.0003238653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001211049,"about_ca_topic_score_gemma":9.187752e-7,"domain_scores_codex":[0.9980702,0.0002555716,0.0006545262,0.0002810243,0.0004156262,0.0003230489],"domain_scores_gemma":[0.9888073,0.009684653,0.0003083802,0.0008958769,0.0002154061,0.00008836512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006252495,0.00001773195,6.326828e-7,0.00007370514,0.0000548078,3.877705e-7,0.00006601165,0.00006213281,0.000763564,0.3834926,0.00247555,0.6129304],"study_design_scores_gemma":[0.000161444,0.00007710452,0.00004816015,0.00003076741,0.00009606921,0.00002006672,0.000005566317,0.5599923,0.001163035,0.4207152,0.01763477,0.00005559952],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002574235,0.00171719,0.9933692,0.002677386,0.001074654,0.0005776978,0.0004850564,0.00001389575,0.00005919409],"genre_scores_gemma":[0.03048134,0.0001233641,0.9689808,0.0001158092,0.0002107295,0.00004462113,0.000006774152,0.00001675807,0.00001972879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6128747,"threshold_uncertainty_score":0.4217795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04164582001596639,"score_gpt":0.3718450544384623,"score_spread":0.3301992344224959,"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."}}