{"id":"W4410545315","doi":"10.23977/jeis.2025.100116","title":"DAGAD: Dual Adversarial Learning Graph Anomaly Detection in Multivariate Time Series Data","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Anomaly detection; Series (stratigraphy); Dual (grammatical number); Graph; Computer science; Adversarial system; Time series; Anomaly (physics); Artificial intelligence; Machine learning; Theoretical computer science; Geology; Physics","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":[0.001513573,0.00006563531,0.0001044585,0.0006574966,0.0002852341,0.0003229255,0.0006396025,0.00003813341,0.000002163237],"category_scores_gemma":[0.0001704069,0.00005820011,0.00002121887,0.001544254,0.00008605547,0.01207092,0.0002581515,0.000258521,0.000002598194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008510562,"about_ca_system_score_gemma":0.0003953624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001469727,"about_ca_topic_score_gemma":0.000007000452,"domain_scores_codex":[0.9990227,0.00002373097,0.000417909,0.0001201501,0.0002407597,0.0001747326],"domain_scores_gemma":[0.9991201,0.00003298259,0.0003304038,0.0002274301,0.0002458457,0.00004319542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001487973,0.0000965082,0.0008206331,0.0000311129,0.00002933377,0.00000281492,0.001513575,0.003417364,0.08245467,0.319856,0.0003298788,0.5912992],"study_design_scores_gemma":[0.001246885,0.0008379416,0.01894253,0.00006531581,0.00001714303,0.0002293832,0.0002477005,0.7588466,0.04396897,0.01132378,0.1639601,0.0003136515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06552947,0.00009284453,0.9319839,0.0005943751,0.0001426606,0.0001082875,0.00000124556,0.00003706842,0.001510082],"genre_scores_gemma":[0.9903194,0.000286755,0.009215709,0.0001085435,0.00001826081,0.000002341464,0.000001260271,0.00000120181,0.00004654037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9247899,"threshold_uncertainty_score":0.8751124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00694633841918223,"score_gpt":0.257554257244163,"score_spread":0.2506079188249808,"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."}}