{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007346898,0.0001552641,0.0001158508,0.0009516312,0.0002614617,0.001396436,0.0001557385,0.0001666077,0.001989885],"category_scores_gemma":[0.003037861,0.00004396692,0.00007828709,0.0008794931,0.0004722839,0.0005856524,0.0004218631,0.0002125045,0.0002568007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000659537,"about_ca_system_score_gemma":0.0005693829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004557316,"about_ca_topic_score_gemma":0.004569123,"domain_scores_codex":[0.9996651,0.00005165684,0.00003125371,0.00003527265,0.0001347745,0.00008193641],"domain_scores_gemma":[0.9949099,0.0008457809,0.003319804,0.0002075176,0.0005161029,0.0002008179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003891609,0.0001677347,0.9499593,0.00004055471,0.00004795714,0.0005211039,0.001076355,0.0009953232,0.005774305,0.002036721,0.0003183878,0.03867304],"study_design_scores_gemma":[0.000002622178,0.00009722084,0.9957519,0.000007827093,0.00001064371,0.00008636105,0.0009898922,0.0005689934,0.001380732,0.000337601,0.0007591931,0.00000705053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99703,0.00006607976,0.00007926206,0.00003519827,0.000001171235,0.000003875572,0.00004596459,0.000002435334,0.002735929],"genre_scores_gemma":[0.9993227,0.00004851927,0.00004977709,0.000006704551,0.00000230629,9.968314e-7,0.00004202635,7.960602e-7,0.0005262026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004557316,"threshold_uncertainty_score":0.009061575,"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."}}