{"id":"W2340675989","doi":"10.1002/cjs.11274","title":"Sample‐size calculation for tests of homogeneity","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Homogeneity (statistics); Sample size determination; Parametric statistics; Statistics; Econometrics; Statistical hypothesis testing; Parametric model; Computer science; Limiting; Simple (philosophy); Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004170734,0.00006370688,0.0001575907,0.000095261,0.00005144079,0.00002446173,0.0003003526,0.0000394582,0.00001469286],"category_scores_gemma":[0.001808674,0.00004519779,0.0000475067,0.0000927892,0.00005236865,0.0001300342,0.000007988726,0.00004187065,5.731785e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006834728,"about_ca_system_score_gemma":0.0009246919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000545143,"about_ca_topic_score_gemma":0.003608219,"domain_scores_codex":[0.9992949,0.00004375404,0.0002994674,0.00008091877,0.0001064049,0.0001745258],"domain_scores_gemma":[0.9976333,0.00116925,0.0002464604,0.0001594326,0.0004651561,0.00032644],"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":[0.000005792557,0.000007775419,0.002509999,0.00002291075,0.0000251215,0.0000237552,0.0002152762,0.00001035621,0.001470013,0.5130782,0.007886436,0.4747443],"study_design_scores_gemma":[0.001038171,0.0004191782,0.05822451,0.0001384989,0.00004611958,0.000108348,0.000004872259,0.00391476,0.002532438,0.922361,0.01097274,0.0002393523],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001300698,0.00007261206,0.9971368,0.0005239537,0.000330239,0.00006200735,0.0005236259,0.00000205356,0.00004796111],"genre_scores_gemma":[0.253096,0.000006964573,0.7467166,0.00006981794,0.0000586687,5.757154e-7,4.338509e-7,0.000004450832,0.00004642243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.474505,"threshold_uncertainty_score":0.2165283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937313448136663,"score_gpt":0.2753395663055425,"score_spread":0.2459664318241758,"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."}}