{"id":"W4409795190","doi":"10.61091/jcmcc127b-522","title":"Construction of Corporate Financial Risk Prediction Model Based on Random Forest Algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Safety and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Anyang Institute of Technology","keywords":"Random forest; Computer science; Financial risk; Finance; Algorithm; Business; Artificial intelligence","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":[0.0008765762,0.0007836896,0.001105423,0.001441894,0.0005343133,0.0007756944,0.001036711,0.0006646424,0.001514398],"category_scores_gemma":[0.001408127,0.0003405605,0.001305994,0.001023427,0.0002637755,0.001135047,0.0005645693,0.0007421319,0.0004934434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490537,"about_ca_system_score_gemma":0.001218377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01953469,"about_ca_topic_score_gemma":0.009325938,"domain_scores_codex":[0.9993944,0.000109435,0.00004240795,0.0001619262,0.0001777079,0.0001140226],"domain_scores_gemma":[0.9995858,0.0001413847,0.00003802841,0.00002410532,0.0001894913,0.00002127315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001250378,0.0001031117,0.009467644,0.00009256868,0.0001037335,0.0002396775,0.00005941142,0.7612141,0.002345199,0.004005587,0.003783193,0.2184607],"study_design_scores_gemma":[0.000006246614,0.00001493168,0.0007031406,0.000006470239,0.00001380856,0.00003714151,0.000007307288,0.9974617,0.0003149841,0.001105398,0.0003210337,0.000007971305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06227678,0.0004947149,0.9326675,0.0002572658,0.00009449761,0.0001071092,0.0003001937,0.001182974,0.002618951],"genre_scores_gemma":[0.7918437,0.0008127182,0.2011349,0.0001147702,0.0000995231,0.0003387196,0.001644019,0.00008352591,0.003928147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01953469,"threshold_uncertainty_score":0.03884196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009658695713306952,"score_gpt":0.2106057704390826,"score_spread":0.2009470747257757,"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."}}