{"id":"W4394938774","doi":"10.5267/j.ijdns.2024.2.013","title":"The role of random forest in internal audit to enhance financial reporting accuracy","year":2024,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Random forest; Audit; Accounting; Finance; Computer science; Artificial intelligence","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":[0.04206428,0.0007851988,0.001104029,0.003409594,0.0009200927,0.002514903,0.001250518,0.001361526,0.001538384],"category_scores_gemma":[0.1365227,0.0003743864,0.0007278801,0.002274782,0.001230455,0.004068139,0.001310218,0.001769443,0.0007975663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037922,"about_ca_system_score_gemma":0.002793306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004997277,"about_ca_topic_score_gemma":0.005990878,"domain_scores_codex":[0.9784997,0.01517352,0.0008293645,0.001394092,0.003350223,0.0007531624],"domain_scores_gemma":[0.8263375,0.1342902,0.01113284,0.01038558,0.0168238,0.001030017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007656638,0.0004140793,0.0524174,0.0003579507,0.0001923123,0.0002390491,0.0006376168,0.2055652,0.007088412,0.02150741,0.005357443,0.7054576],"study_design_scores_gemma":[0.00005758096,0.0002933764,0.009286244,0.0002177472,0.00008744001,0.0002120069,0.0002146161,0.9437495,0.008089926,0.03430677,0.003386717,0.0000980633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1008178,0.0009790666,0.8891707,0.002321275,0.0002000454,0.0002420688,0.0002384712,0.00216154,0.003869002],"genre_scores_gemma":[0.6417527,0.0002600767,0.356338,0.0002630047,0.0001376255,0.000122338,0.0002135875,0.0001696501,0.0007430866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04206428,"threshold_uncertainty_score":0.2224599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08475796216158646,"score_gpt":0.4723161147597445,"score_spread":0.3875581525981581,"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."}}