{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00144374,0.0004070874,0.0005296566,0.0002181649,0.003066641,0.001388499,0.002206228,0.0002988452,0.00000744955],"category_scores_gemma":[0.00001615481,0.0004413059,0.0001239894,0.001430646,0.0004489342,0.0005871301,0.002104423,0.0006674493,0.00005721885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519729,"about_ca_system_score_gemma":0.0002099301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002236538,"about_ca_topic_score_gemma":0.000008101984,"domain_scores_codex":[0.9968842,0.0003469352,0.001122893,0.0007678405,0.0002760606,0.0006020883],"domain_scores_gemma":[0.9958898,0.0002797173,0.0006574037,0.002518818,0.0005182797,0.0001360167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008954125,0.0002695514,0.08007468,0.0005073471,0.0002670005,0.000001435866,0.00184741,0.0475032,0.00004225156,0.4893403,0.01005079,0.3700065],"study_design_scores_gemma":[0.0003758248,0.0001568233,0.06429459,0.0010063,0.00003063488,0.00002890601,0.0001606665,0.8908266,0.00005818488,0.0005168933,0.04214733,0.0003972796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2168027,0.01552045,0.7274738,0.0007363198,0.001056434,0.001217598,0.00000557156,0.0006309241,0.03655627],"genre_scores_gemma":[0.9818997,0.002521425,0.01078821,0.0003145906,0.0002328941,0.0001135789,0.000008456643,0.00002055512,0.004100544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8433233,"threshold_uncertainty_score":0.9998039,"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."}}