{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004609461,0.0007376219,0.0006777227,0.002136009,0.0004778359,0.0008982526,0.0006047038,0.0004429304,0.002251114],"category_scores_gemma":[0.01476363,0.0002562988,0.0007573994,0.0024104,0.0003168689,0.0009268541,0.0006760467,0.001184086,0.0007914553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002565112,"about_ca_system_score_gemma":0.0008401629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005080097,"about_ca_topic_score_gemma":0.004600241,"domain_scores_codex":[0.9971405,0.002161898,0.00009398648,0.0002316809,0.0002094275,0.0001624608],"domain_scores_gemma":[0.9947355,0.004005132,0.0002642649,0.0003133133,0.0005695337,0.0001121354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000926298,0.0004636733,0.3860524,0.0001409058,0.0006115086,0.0006933874,0.0002954986,0.07706175,0.001921163,0.006768243,0.007181928,0.5178833],"study_design_scores_gemma":[0.00005054675,0.0001483488,0.101559,0.00004905775,0.0001587455,0.0002244357,0.0002397721,0.888745,0.001392767,0.005232628,0.002144506,0.00005517163],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6479968,0.001470673,0.3435373,0.0008888453,0.0002185027,0.0002440454,0.000563974,0.0006466923,0.004433225],"genre_scores_gemma":[0.9559413,0.0004072513,0.04072189,0.0000401719,0.00009782307,0.0000890375,0.0006258525,0.00004598244,0.002030873],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005080097,"threshold_uncertainty_score":0.02437747,"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."}}