{"id":"W1517573887","doi":"10.1108/raf-07-2014-0071","title":"Do changes in gross margin percentage provide complementary information to revenue and earnings surprises?","year":2015,"lang":"en","type":"article","venue":"Review of Accounting and Finance","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Gross margin; Earnings; Revenue; Margin (machine learning); Gross profit; Quarter (Canadian coin); Profit margin; Economics; Gross output; Econometrics; Monetary economics; Profit (economics); Accounting; Finance; Macroeconomics; Profitability index","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001983575,0.000398473,0.0005697843,0.001643366,0.0001595373,0.001696651,0.0006257046,0.0008027373,0.00372976],"category_scores_gemma":[0.01857418,0.0002147925,0.0005008462,0.001489485,0.00064318,0.002027782,0.0006975231,0.0008517961,0.001215849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004088778,"about_ca_system_score_gemma":0.0004756153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001838554,"about_ca_topic_score_gemma":0.001615593,"domain_scores_codex":[0.998716,0.0002552931,0.0001560321,0.0002976799,0.0004247707,0.0001501769],"domain_scores_gemma":[0.9628408,0.01202515,0.02164224,0.001184925,0.001466081,0.0008407501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000397224,0.0001080384,0.9830429,0.00007537861,0.0001310615,0.0002040719,0.00009775782,0.0007772919,0.0006248861,0.0005237162,0.0003872458,0.0136305],"study_design_scores_gemma":[0.000009439495,0.0002188249,0.9924319,0.00003827891,0.00008519243,0.000194761,0.0003950846,0.003701562,0.0008893925,0.001152925,0.0008636333,0.00001898071],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993107,0.001089545,0.001033021,0.0006082954,0.0000397722,0.00002131221,0.0009105446,0.00002864247,0.003161941],"genre_scores_gemma":[0.9985177,0.0002873769,0.0001911338,0.0000608499,0.00007980769,0.000004625211,0.0004915834,0.000005290788,0.000361652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00372976,"threshold_uncertainty_score":0.01247728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474468214671966,"score_gpt":0.2413165824444367,"score_spread":0.226571900297717,"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."}}