{"id":"W3121991680","doi":"10.1111/j.1911-3846.2010.01027.x","title":"The Association Between Accruals Quality and the Characteristics of Accounting Experts and Mix of Expertise on Audit Committees*","year":2010,"lang":"en","type":"article","venue":"Contemporary Accounting Research","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":640,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Audit; Accrual; Library science; Accounting; Citation; Management; Quality (philosophy); Political science; Business; Economics; Earnings; Computer science; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01173296,0.0002941464,0.0004439057,0.002962396,0.0005555252,0.003300535,0.0007652143,0.001144932,0.008083722],"category_scores_gemma":[0.06470284,0.0003151498,0.0005925603,0.003097666,0.001384172,0.001893928,0.001477237,0.001632541,0.001944679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000762368,"about_ca_system_score_gemma":0.0007292528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00191856,"about_ca_topic_score_gemma":0.002969806,"domain_scores_codex":[0.9942288,0.001877106,0.001113398,0.0006149341,0.001512128,0.0006535937],"domain_scores_gemma":[0.7911738,0.07708492,0.09948783,0.008278842,0.0144904,0.009484233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009985122,0.00004570353,0.9942421,0.00001592573,0.00006458904,0.00002204551,0.0001306232,0.0001716191,0.00007696333,0.0002552671,0.0003239755,0.004551271],"study_design_scores_gemma":[0.000007086493,0.00007360812,0.9976719,0.00001500458,0.00002699342,0.0001462619,0.000250669,0.0004107987,0.00008765723,0.0006741385,0.0006275412,0.00000825132],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900509,0.001427793,0.0008168028,0.001117369,0.00004024702,0.00002393355,0.0004165025,0.00004038389,0.006066045],"genre_scores_gemma":[0.9983268,0.0001530999,0.0002355202,0.00009026909,0.00006059177,0.000004665688,0.0002496182,0.00001235174,0.0008671201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01173296,"threshold_uncertainty_score":0.06205052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05064963337846415,"score_gpt":0.3226380755911024,"score_spread":0.2719884422126383,"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."}}