{"id":"W4413932916","doi":"10.3390/jrfm18090488","title":"Risk Prediction of International Stock Markets with Complex Spatio-Temporal Correlations: A Spatio-Temporal Graph Convolutional Regression Model Integrating Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Regression; Stock (firearms); Econometrics; Graph; Artificial intelligence; Data mining; Theoretical computer science; Mathematics; Geography; Statistics","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":[0.0007993093,0.0001740803,0.0003695659,0.0005170053,0.0001765497,0.00002921895,0.0001328059,0.00007122485,0.00003008882],"category_scores_gemma":[0.0003910172,0.0001348889,0.0001130485,0.0003939022,0.0001388664,0.0002483449,0.00006745323,0.0003033467,8.481015e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001479615,"about_ca_system_score_gemma":0.0002172488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001375208,"about_ca_topic_score_gemma":0.0002226601,"domain_scores_codex":[0.9981002,0.0001273939,0.0008538596,0.0002368729,0.0005485445,0.0001331565],"domain_scores_gemma":[0.9976248,0.00009268187,0.001238739,0.0002071683,0.0007547403,0.00008194162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004549562,0.0003470341,0.9220426,0.0001426219,0.0001973004,0.00001596982,0.0001905029,0.008893413,0.00001627515,0.003704161,0.008101149,0.05179939],"study_design_scores_gemma":[0.002684382,0.0001932892,0.7974219,0.0008085634,0.0003108639,0.00001106214,0.0002218999,0.1890745,0.000005512595,0.002391661,0.006786119,0.00009022209],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4999459,0.00034121,0.4973621,0.0002306316,0.0003812466,0.0004955992,0.0007069913,0.00002394101,0.0005122872],"genre_scores_gemma":[0.9786876,0.0009871734,0.01915737,0.00004478367,0.00009553305,0.00001574285,0.0008724278,0.000009907309,0.00012946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4787417,"threshold_uncertainty_score":0.550061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01647359962724146,"score_gpt":0.2661785216987703,"score_spread":0.2497049220715289,"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."}}