{"id":"W4391611445","doi":"10.1002/for.3074","title":"Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Particle swarm optimization; Machine learning; Optimal decision; Binary decision diagram; Credit risk; Data mining; Artificial intelligence; Decision tree; Finance; Algorithm; Business","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":[0.0009746987,0.0002093369,0.0002594338,0.0005192026,0.0004379877,0.0006453298,0.0001777249,0.00009845686,0.0001848115],"category_scores_gemma":[0.001164699,0.0001482184,0.0001777017,0.000710048,0.00004131964,0.001155426,0.00004385999,0.0009984813,0.00003095756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007846831,"about_ca_system_score_gemma":0.00006026776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005364212,"about_ca_topic_score_gemma":0.00004302304,"domain_scores_codex":[0.9982356,0.00001552695,0.0005799544,0.0002190323,0.0006744375,0.0002754161],"domain_scores_gemma":[0.9985446,0.0003993003,0.000656869,0.0001431652,0.0002281506,0.00002785308],"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":[0.0004241063,0.0001961675,0.1977469,0.0004777978,0.0001593533,0.0005597817,0.0001479696,0.486738,0.00007315491,0.009984314,0.00281083,0.3006816],"study_design_scores_gemma":[0.0004921375,0.0002260931,0.03414441,0.00439284,0.0001863787,0.00002393758,0.0001221682,0.9529928,0.000008889144,0.003235594,0.004022936,0.0001518409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102238,0.0002063837,0.1804032,0.0002148131,0.001852839,0.0001617145,0.00001249492,0.0002344234,0.006690375],"genre_scores_gemma":[0.9927676,0.00001140583,0.003179193,0.00009992565,0.003833913,0.000005957221,0.00001007762,0.00004452419,0.00004740764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4662548,"threshold_uncertainty_score":0.6222931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02629509532868281,"score_gpt":0.2388271137773722,"score_spread":0.2125320184486894,"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."}}