{"id":"W4416809495","doi":"10.5267/j.ac.2025.9.004","title":"Financial risk early warning of airlines based on convolutional neural network models","year":2025,"lang":"en","type":"article","venue":"Accounting","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Warning system; Prosperity; Sample (material); Financial risk; Early warning system; Data pre-processing; Preprocessor; Risk management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003891367,0.0001803832,0.0002327159,0.0002130118,0.0004082134,0.0001303713,0.0002014783,0.0001070147,0.00003268061],"category_scores_gemma":[0.0003355371,0.0001773531,0.0001299054,0.0006564798,0.00006194767,0.0008598752,0.0001047588,0.0002302632,0.00001822512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002654737,"about_ca_system_score_gemma":0.0000561091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001409177,"about_ca_topic_score_gemma":0.0001011402,"domain_scores_codex":[0.998715,0.00001254065,0.0003892283,0.0002834746,0.0002760256,0.0003236991],"domain_scores_gemma":[0.9990816,0.00009413391,0.0003829259,0.0001804158,0.0002561845,0.000004773865],"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":[0.0001461511,0.00006619204,0.3660143,0.0001288008,0.00001131774,0.000002481053,0.00001071744,0.5469815,0.00002856788,0.0728147,0.004157353,0.009637973],"study_design_scores_gemma":[0.0003965154,0.00000871895,0.3367158,0.0001938125,0.00004598859,6.866382e-8,0.00001193271,0.6500068,0.000008598961,0.01065653,0.001834099,0.0001211689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734811,0.00009623144,0.01292043,0.0001936998,0.001253225,0.0001789295,0.00001245746,0.0001504644,0.01171352],"genre_scores_gemma":[0.9966111,0.000003321677,0.000233838,0.001175254,0.001843473,0.00001793475,0.00004218486,0.00001576008,0.00005710214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1030253,"threshold_uncertainty_score":0.7232249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008926577974195087,"score_gpt":0.1984527583897632,"score_spread":0.1895261804155682,"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."}}