{"id":"W2348926810","doi":"","title":"Logistic Regression Based on Principal Component Analysis in Resolving Credit Risk Discrimination of Corporate","year":2009,"lang":"en","type":"article","venue":"Journal of Henan Institute of Engineering","topic":"Evaluation and Optimization Models","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Logistic regression; Quarter (Canadian coin); Sample (material); Econometrics; Regression analysis; Principal component regression; Credit risk; Stock (firearms); Statistics; Actuarial science; Business; Economics; Mathematics; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005881042,0.0001282506,0.0003710504,0.00107762,0.0000182375,0.000011132,0.0001281223,0.00006034627,0.00001068217],"category_scores_gemma":[0.0002726442,0.000111427,0.0001299942,0.000606978,0.00001915838,0.000231524,0.000007295648,0.0002192986,3.001285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012356,"about_ca_system_score_gemma":0.00003860422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004612401,"about_ca_topic_score_gemma":0.000006930146,"domain_scores_codex":[0.9986168,0.00002867383,0.0007787986,0.0000799701,0.0003777076,0.0001181242],"domain_scores_gemma":[0.9990549,0.0000584246,0.0004991145,0.0001477235,0.0001777431,0.00006208035],"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":[0.0000376294,0.00005678948,0.00140047,0.00006416054,0.00004841854,0.00001075551,0.0001153207,0.99355,0.003540493,0.0003928556,0.0000148253,0.0007682604],"study_design_scores_gemma":[0.000599733,0.0001084028,0.04753558,0.0004716298,0.0001258403,0.000001826142,0.00001117458,0.9484758,0.00248843,0.00005361599,0.00003375364,0.00009418434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4832235,0.000126489,0.5160633,0.00003692652,0.0002848167,0.00006944968,0.000005975841,0.00001920907,0.0001703187],"genre_scores_gemma":[0.9700221,0.0001078806,0.02979857,0.000006303479,0.00004164026,7.608754e-7,0.000009991306,0.000009630528,0.000003149791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4867986,"threshold_uncertainty_score":0.4543859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191695639207076,"score_gpt":0.2702143734657084,"score_spread":0.2282974170736376,"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."}}