{"id":"W4399133254","doi":"10.4236/ojs.2024.143010","title":"Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions","year":2024,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Bivariate analysis; Mathematics; Statistics; Multivariate normal distribution; Random noise; SIGNAL (programming language); Multivariate statistics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01563055,0.00195925,0.001244935,0.004178837,0.0007663749,0.002623907,0.002959129,0.002319718,0.01072449],"category_scores_gemma":[0.09036434,0.0008471936,0.002333689,0.005930946,0.002482908,0.00317411,0.002628739,0.003418657,0.009011881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205615,"about_ca_system_score_gemma":0.001695091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004438015,"about_ca_topic_score_gemma":0.00252836,"domain_scores_codex":[0.9892853,0.005463763,0.0007707981,0.001313092,0.00285916,0.0003078989],"domain_scores_gemma":[0.9564251,0.0272,0.003605012,0.007055821,0.005438357,0.0002755677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003075233,0.0001371151,0.01481121,0.001120811,0.0002769411,0.001316181,0.001579481,0.18634,0.009462561,0.2430962,0.03063776,0.5109143],"study_design_scores_gemma":[0.0000388217,0.0002347527,0.006983934,0.0003139206,0.00007143866,0.001849644,0.0002481129,0.8078681,0.00833071,0.1156106,0.05822553,0.0002244523],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002716182,0.0002500406,0.9932936,0.0001700488,0.00006561026,0.00005895933,0.0002239369,0.001313628,0.001907948],"genre_scores_gemma":[0.09344555,0.0009548164,0.8962861,0.0002993812,0.00009223344,0.0005237281,0.0009353673,0.00136588,0.006096916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01563055,"threshold_uncertainty_score":0.08266318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1771917334711863,"score_gpt":0.4340734179113566,"score_spread":0.2568816844401702,"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."}}