{"id":"W4415530604","doi":"10.1016/j.cie.2025.111616","title":"Enhancing data anomaly prediction and real-time physical problem detection with Digital Twins and Cognitive Super Digital Twins","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Anomaly detection; Resilience (materials science); Cyber-physical system; Cognition; Robot; Recall; Cognitive map; Layer (electronics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00222032,0.0009905442,0.0008855293,0.001849859,0.0005154008,0.00181687,0.001684077,0.0009082538,0.001499551],"category_scores_gemma":[0.01137901,0.000392328,0.0008908521,0.001112671,0.001209957,0.004724881,0.003553649,0.001649618,0.0003788932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007319845,"about_ca_system_score_gemma":0.001407907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765833,"about_ca_topic_score_gemma":0.003409991,"domain_scores_codex":[0.9986822,0.0002938586,0.0001072613,0.0003677534,0.0004435724,0.000105362],"domain_scores_gemma":[0.9953154,0.001853371,0.0004769321,0.001242103,0.0008943488,0.0002176956],"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":[0.0005786719,0.0007120436,0.02538035,0.0003398067,0.0002563783,0.0004541625,0.0006289099,0.2818738,0.02752598,0.04280429,0.002940586,0.616505],"study_design_scores_gemma":[0.00001043172,0.0001420101,0.001574492,0.00001587279,0.0000313827,0.0001928347,0.00009622081,0.966224,0.00795433,0.02195667,0.001776634,0.00002503854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05091686,0.0001676384,0.9457093,0.0002872949,0.00007159941,0.00007195811,0.00009265086,0.0008206451,0.001862049],"genre_scores_gemma":[0.6789686,0.0002024819,0.3185573,0.000198871,0.00005425287,0.00009868584,0.000328577,0.0001204559,0.001470922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002765833,"threshold_uncertainty_score":0.01174229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289408184608385,"score_gpt":0.1995227134792217,"score_spread":0.1866286316331379,"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."}}