{"id":"W3166630008","doi":"10.2139/ssrn.3833200","title":"Revisit the Use of Asset Turnover and Profit Margin in Forecasting Operating Profitability: Further Evidence","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Reporting and Valuation Research","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Profitability index; Asset turnover; Profit margin; Margin (machine learning); Business; Operating margin; Profit (economics); Asset (computer security); Economics; Industrial organization; Microeconomics; Monetary economics; Finance; Computer science; Return on assets","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006923249,0.0001011618,0.0001741561,0.000100977,0.0001941415,0.0003736665,0.0001302994,0.00004379413,0.0000437632],"category_scores_gemma":[0.007741454,0.00007103647,0.00005474293,0.0005441244,0.00004400641,0.0009790938,0.0001422716,0.001003632,0.000004098867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001444608,"about_ca_system_score_gemma":0.001145127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003862053,"about_ca_topic_score_gemma":0.001687275,"domain_scores_codex":[0.9979862,0.00009227059,0.0004778277,0.0002027845,0.0004004923,0.0008404153],"domain_scores_gemma":[0.9987512,0.0002421387,0.00033723,0.0001633464,0.0004968401,0.000009211773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004388128,0.00003515826,0.8833474,0.0001892514,0.00003230459,0.00002268305,0.000181105,0.00020604,0.0015762,0.03061578,0.00009886736,0.08365134],"study_design_scores_gemma":[0.002628974,0.0002305904,0.7429733,0.004240206,0.000225735,0.001052936,0.00896345,0.09943662,0.0007893483,0.1265276,0.0118576,0.001073577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928898,0.001799782,0.0007391495,0.004028733,0.00006030253,0.0002343237,2.752184e-7,0.000008225612,0.000239426],"genre_scores_gemma":[0.9985433,0.0002341635,0.0002223236,0.0001583444,0.0002777393,0.000009959868,0.000001494866,0.00001397778,0.0005387606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.140374,"threshold_uncertainty_score":0.9267802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1420889928211695,"score_gpt":0.3172105210458172,"score_spread":0.1751215282246477,"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."}}