{"id":"W3157920145","doi":"10.3390/jrfm14050199","title":"Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bankruptcy; Creditor; Financial distress; Business; Cash flow; Shareholder; Sample (material); Actuarial science; Going concern; Distress; Probability of default; Affect (linguistics); Finance; Accounting; Debt; Financial system; Credit risk; Corporate governance; Audit; Psychology","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":[0.007840443,0.0008941026,0.000852351,0.00374377,0.000748921,0.001604298,0.001258575,0.001438211,0.003184688],"category_scores_gemma":[0.0232296,0.0002730299,0.001466824,0.003918516,0.0007495047,0.002086611,0.0010221,0.002168027,0.0008072832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009642535,"about_ca_system_score_gemma":0.0009599184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02134075,"about_ca_topic_score_gemma":0.007821046,"domain_scores_codex":[0.9978862,0.0008966084,0.000137904,0.0002916048,0.000428035,0.0003596086],"domain_scores_gemma":[0.9690815,0.0253703,0.002082125,0.0008295579,0.001557224,0.001079284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002798192,0.0006345508,0.9455295,0.00005122958,0.0002800797,0.0002995535,0.0002596701,0.0242929,0.0001254259,0.0008860331,0.002146403,0.02521481],"study_design_scores_gemma":[0.00004327797,0.0007675388,0.5287319,0.00007315791,0.0002277962,0.0003079925,0.001035817,0.4653905,0.0002704994,0.001924939,0.001166478,0.00006013262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992364,0.0003500608,0.005046763,0.0003557437,0.00002799889,0.00005206902,0.0005287584,0.00005494693,0.001219581],"genre_scores_gemma":[0.9975851,0.0001229269,0.001159981,0.00001944151,0.00002360635,0.00002049977,0.0007044554,0.000005661256,0.0003582796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02134075,"threshold_uncertainty_score":0.04243308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02148601671256615,"score_gpt":0.2497874355926666,"score_spread":0.2283014188801005,"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."}}