{"id":"W2984220243","doi":"10.5267/j.msl.2019.11.012","title":"Operating performance and manipulation of accruals","year":2019,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Accrual; Computer science; Business; Process management; Accounting","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":[],"consensus_categories":[],"category_scores_codex":[0.003064247,0.00006908671,0.000144407,0.0004536841,0.000182126,0.0002113722,0.0006416345,0.00001122242,0.0001362597],"category_scores_gemma":[0.00007335664,0.00005050593,0.00003437923,0.001442214,0.0002148724,0.0009335882,0.000261341,0.00003943879,0.0001546483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001853108,"about_ca_system_score_gemma":0.00000346289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001069954,"about_ca_topic_score_gemma":0.000001030556,"domain_scores_codex":[0.9977405,0.00003468338,0.0003439159,0.0004019751,0.001291143,0.0001878045],"domain_scores_gemma":[0.9992704,0.00008087942,0.0001473571,0.000424415,0.00003729805,0.0000396141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004106862,0.0000120599,0.8638736,0.00001877937,0.00001155477,0.000001615806,0.0005173732,0.02492385,0.02436933,0.003253929,0.0007023503,0.08231147],"study_design_scores_gemma":[0.0001512038,0.00002150825,0.9136837,0.00001534729,0.000009734827,7.686788e-7,0.0007196735,0.08275618,0.001169275,0.0003813718,0.0009959475,0.0000953019],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844017,0.000009648595,0.003411313,0.002186534,0.0001183773,0.0001383036,3.180921e-7,0.000008891309,0.009724932],"genre_scores_gemma":[0.99565,0.00002883925,0.002420266,0.0008074829,0.000009771544,0.000002086929,3.47196e-7,0.000002249411,0.001078962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08221617,"threshold_uncertainty_score":0.2059572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04475002232283628,"score_gpt":0.3239848574254789,"score_spread":0.2792348351026427,"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."}}