{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005993067,0.0007010985,0.0003216861,0.0008529332,0.0001566347,0.0007036203,0.0004986649,0.0005320196,0.0009576196],"category_scores_gemma":[0.002287023,0.0002176353,0.0003061517,0.0004694954,0.0001709202,0.0008536039,0.0004342034,0.0007839391,0.0002010879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008836974,"about_ca_system_score_gemma":0.0006217507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02094503,"about_ca_topic_score_gemma":0.01977303,"domain_scores_codex":[0.9998658,0.00002816541,0.0000102177,0.00002853288,0.00002960873,0.00003764516],"domain_scores_gemma":[0.9994817,0.0002376438,0.0001027126,0.00002783157,0.0001199771,0.00003018462],"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":[0.0003796576,0.0001722936,0.02902517,0.00007404903,0.0001076431,0.0001876887,0.0000518759,0.850904,0.004613226,0.002375831,0.003162025,0.1089466],"study_design_scores_gemma":[0.00000160491,0.00001190068,0.001836551,0.000007718837,0.000006936292,0.000006106068,0.00000490517,0.9970518,0.0006181077,0.0003526472,0.00009845159,0.000003198764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7485128,0.001686451,0.2380423,0.001358558,0.0001761665,0.00009689725,0.001174357,0.001324849,0.007627664],"genre_scores_gemma":[0.9883867,0.0002896879,0.009148917,0.00005306834,0.0000276538,0.00001944606,0.0005711329,0.000009395299,0.001493867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02094503,"threshold_uncertainty_score":0.04164624,"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."}}