{"id":"W2769222633","doi":"10.6000/1927-5129.2017.13.96","title":"Impact of Logarithmic Transformation on the Restoration of Normality in Bioequivalence Data","year":2017,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normality; Mathematics; Statistics; Skewness; Normal distribution; Estimator; Transformation (genetics); Bioequivalence; Data transformation; Normality test; Econometrics; Statistical hypothesis testing; Kurtosis; Computer science; Data mining; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01760295,0.00007885762,0.0003879677,0.00009898469,0.0001348313,0.00004874732,0.001500706,0.00005544672,0.00004383591],"category_scores_gemma":[0.02405493,0.00004187354,0.00008128597,0.0001679783,0.0008363623,0.0003775121,0.0000758579,0.0002245575,0.000001138186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003544571,"about_ca_system_score_gemma":0.0002346864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003419693,"about_ca_topic_score_gemma":0.00001667355,"domain_scores_codex":[0.9976594,0.0002724784,0.001136589,0.0001234787,0.0006832763,0.000124704],"domain_scores_gemma":[0.9888801,0.0083344,0.002016986,0.0006091516,0.0001204344,0.00003891036],"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":[0.001669951,0.001362334,0.02020844,0.0003929494,0.0001487445,0.000006044648,0.002625804,0.0005763197,0.04665626,0.8076886,0.00120583,0.1174587],"study_design_scores_gemma":[0.0008545427,0.0008094675,0.1119339,0.0002629685,0.00005460237,0.000004364626,0.0002921906,0.00193689,0.01078858,0.872961,0.000008838395,0.00009270637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704927,0.000008316555,0.02508033,0.0007573261,0.0001801285,0.0002443887,0.00005286155,0.000002058729,0.003181862],"genre_scores_gemma":[0.9262034,0.00002435462,0.07369514,0.00001341812,0.00005845886,0.000001201994,1.790782e-7,0.000002489181,0.000001307095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.117366,"threshold_uncertainty_score":0.9841658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7586372073922317,"score_gpt":0.6109137437331346,"score_spread":0.1477234636590972,"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."}}