{"id":"W2149641584","doi":"10.2307/3315931","title":"Estimating the number of classes in multiple populations: A geometric analysis","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Confidence interval; Multivariate statistics; Inference; Statistics; Mathematics; Poisson distribution; Nonparametric statistics; Class (philosophy); Point estimation; Odds; Point (geometry); Multivariate analysis; Computer science; Logistic regression; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004327102,0.00007558204,0.0002422863,0.0006646875,0.00009434953,0.00002765714,0.0001185361,0.00004021066,0.0001086729],"category_scores_gemma":[0.003413995,0.00005979403,0.00006830334,0.001427187,0.00005173356,0.00009136312,0.000004462257,0.0001406426,0.000002362127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001872251,"about_ca_system_score_gemma":0.0004182436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01030991,"about_ca_topic_score_gemma":0.1142391,"domain_scores_codex":[0.9988623,0.00004324778,0.0006961863,0.00005569074,0.0001964263,0.0001461764],"domain_scores_gemma":[0.9983639,0.0004824917,0.0005716901,0.0001262018,0.0003291112,0.000126564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004027529,0.00002778204,0.751524,0.00004110266,0.00009336502,0.00002895287,0.0008504066,0.1664293,0.000001772002,0.07799944,0.0003913718,0.002608465],"study_design_scores_gemma":[0.0005230763,0.00002205816,0.6994849,0.00008412555,0.0003531869,0.00005004345,0.0001692488,0.04518889,0.000007479289,0.2539504,0.00006683911,0.00009973774],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5077271,0.00004587584,0.4916358,0.0001664515,0.0001364405,0.00006853059,0.0001342253,0.000001900609,0.00008363846],"genre_scores_gemma":[0.6801198,0.000001509268,0.3198071,0.00001525967,0.00002871483,6.77237e-7,0.0000119859,0.000006192152,0.000008827447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1759509,"threshold_uncertainty_score":0.9962805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08934764143855863,"score_gpt":0.3430036937872819,"score_spread":0.2536560523487233,"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."}}