{"id":"W2030195324","doi":"10.1007/s00780-006-0033-1","title":"Information reduction via level crossings in a credit risk model","year":2007,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Mathematical finance; Asset (computer security); Credit risk; Coupon; Reduction (mathematics); Bond market; Economics; Bond; Value (mathematics); Class (philosophy); Default risk; Actuarial science; Financial economics; Microeconomics; Econometrics; Business; Monetary economics; Finance; Computer science; Mathematics; Statistics; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004910361,0.0001191132,0.000215188,0.0002812687,0.0001981318,0.00006249411,0.00007388046,0.0001389601,0.000005546131],"category_scores_gemma":[0.0001507101,0.0001480383,0.00004088914,0.0002947164,0.00008930764,0.0005974303,0.00003001911,0.0001920522,0.00005195415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008015219,"about_ca_system_score_gemma":0.00002723827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003631636,"about_ca_topic_score_gemma":0.0001079395,"domain_scores_codex":[0.9989439,0.000002217471,0.000576507,0.0001655481,0.00004219214,0.0002696375],"domain_scores_gemma":[0.9994205,0.00002523371,0.0003077505,0.0001575559,0.00004647104,0.00004246522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002677795,0.0001982617,0.0630172,0.0000581906,0.00001788244,0.000004376795,0.01506795,0.07782464,0.00005685579,0.6476411,0.002501403,0.1933444],"study_design_scores_gemma":[0.0008014272,0.00007364144,0.5813262,0.00002629401,0.000005377245,0.00001012216,0.0001319646,0.313747,0.00002573075,0.08764409,0.0158998,0.0003083909],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4286237,0.000226501,0.5693462,0.00004799352,0.0002403888,0.00009752035,0.0001592089,0.0000140025,0.001244504],"genre_scores_gemma":[0.9921124,0.0002366992,0.007079647,0.00001775446,0.0001582342,0.00001094551,0.00003111573,0.000009373943,0.0003438215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5634887,"threshold_uncertainty_score":0.6036828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784627042767331,"score_gpt":0.2285741678842583,"score_spread":0.200727897456585,"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."}}