{"id":"W1503655999","doi":"10.1186/1471-2288-6-24","title":"Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables","year":2006,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; King Faisal Specialist Hospital and Research Centre","keywords":"Statistics; Sample size determination; Confidence interval; Mathematics; Variance (accounting); Coefficient of variation; Interval estimation; Transformation (genetics); Reliability (semiconductor); Binary number; Variable (mathematics); Random variable; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04235991,0.000106475,0.0004275114,0.0001120559,0.0001588836,0.00003145623,0.0001839681,0.0001902106,0.00004115856],"category_scores_gemma":[0.2123479,0.00006731136,0.00004056257,0.0001504822,0.0007502072,0.00003212844,0.0001150635,0.0002240857,2.726841e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002181904,"about_ca_system_score_gemma":0.0003292051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001382513,"about_ca_topic_score_gemma":0.00002351977,"domain_scores_codex":[0.9931438,0.005203208,0.0005237442,0.0002951481,0.0004904575,0.0003436485],"domain_scores_gemma":[0.7233116,0.2759741,0.000131391,0.0001538359,0.0003344812,0.00009456815],"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":[0.0008866803,0.0001110396,0.00005424688,0.0006320901,0.00003222709,0.000001762051,0.000478445,0.0001434906,0.001722421,0.9270322,0.000707973,0.06819744],"study_design_scores_gemma":[0.0006115747,0.0006934634,0.0005148361,0.00007076116,0.0000393833,0.0000139854,0.0001316916,0.4755017,0.0009742908,0.5213584,0.00004137935,0.00004858578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007324916,0.000197559,0.9906676,0.0004157639,0.000121383,0.001202886,0.00002597786,0.00001513862,0.00002878439],"genre_scores_gemma":[0.02988682,0.00002449926,0.9696184,0.0000228422,0.00009511394,0.0002945718,0.000004518979,0.00001412262,0.00003905927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4753582,"threshold_uncertainty_score":0.986092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4294446816876692,"score_gpt":0.5289955412166452,"score_spread":0.09955085952897591,"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."}}