{"id":"W7118166196","doi":"10.23977/cpcs.2025.090114","title":"ACMAN: Adaptive Cross-Modal Anomaly Network","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Anomaly (physics); Inference; Feature (linguistics); Representation (politics); Generative grammar; Quality (philosophy)","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":[0.001777313,0.001221367,0.0007513934,0.001368296,0.0007347402,0.0009414456,0.002524081,0.001287789,0.002526719],"category_scores_gemma":[0.003896784,0.000411201,0.000945347,0.0009071049,0.0009569397,0.00195251,0.002952168,0.002078545,0.000738075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116344,"about_ca_system_score_gemma":0.001281333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009721982,"about_ca_topic_score_gemma":0.01309817,"domain_scores_codex":[0.9986956,0.0003317711,0.00003708253,0.000450084,0.0003423301,0.0001433675],"domain_scores_gemma":[0.9985169,0.0005599668,0.0001185922,0.0002940261,0.0004300643,0.00008054478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004265673,0.000213817,0.003761298,0.0001361363,0.000210244,0.0001874724,0.00019627,0.396624,0.01665894,0.02558902,0.01760804,0.5383882],"study_design_scores_gemma":[0.000006093595,0.00002683799,0.000272803,0.000004606452,0.00001004369,0.00004528659,0.00001274377,0.986013,0.002322756,0.009253864,0.002020986,0.00001105744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01475064,0.0002596483,0.9770814,0.0002330664,0.00009962212,0.00007217831,0.0003676647,0.005366708,0.001769055],"genre_scores_gemma":[0.5038195,0.0002336907,0.4839964,0.0007505544,0.0001561901,0.000266224,0.002984768,0.0009284591,0.00686416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009721982,"threshold_uncertainty_score":0.0193308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01955614023322197,"score_gpt":0.2880982924677818,"score_spread":0.2685421522345599,"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."}}