{"id":"W4236348463","doi":"10.32920/ryerson.14660505.v1","title":"An empirical application of data envelopment analysis in credit rating","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Data envelopment analysis; Estimator; Principal component analysis; Credit rating; Econometrics; Sample (material); Nonparametric statistics; Credit risk; Envelopment; Economics; Computer science; Statistics; Actuarial science; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008033492,0.0007240647,0.0008132674,0.002355542,0.0003941801,0.001973627,0.0005676675,0.0008065903,0.001146246],"category_scores_gemma":[0.04093821,0.0003249187,0.001129249,0.005195097,0.0007420912,0.002012985,0.001257246,0.001175758,0.000260523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001404079,"about_ca_system_score_gemma":0.00134965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006252502,"about_ca_topic_score_gemma":0.002367949,"domain_scores_codex":[0.9946393,0.003307069,0.0002657612,0.0004166153,0.001197067,0.0001741805],"domain_scores_gemma":[0.9866341,0.009387053,0.001027684,0.001496619,0.001326332,0.0001280859],"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":[0.0001109524,0.0002105539,0.05839273,0.000343502,0.0003058932,0.0002074552,0.0007532254,0.6306114,0.001791545,0.1503472,0.002123943,0.1548016],"study_design_scores_gemma":[0.000009885412,0.0001025522,0.01585132,0.0001041248,0.00002964825,0.00007141661,0.0002624842,0.9396155,0.0009987169,0.03980858,0.003106153,0.00003971542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2603154,0.001914599,0.7225155,0.00109735,0.00005943651,0.0002478333,0.0008041365,0.0002023288,0.01284354],"genre_scores_gemma":[0.931456,0.0006841541,0.06682061,0.0000412169,0.00001967514,0.00009975438,0.0003064805,0.00001812933,0.0005540478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008033492,"threshold_uncertainty_score":0.04248571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2345122328503667,"score_gpt":0.4957001604201922,"score_spread":0.2611879275698256,"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."}}