{"id":"W4414697638","doi":"10.1016/j.meaene.2025.100068","title":"Temperature measurement uncertainty quantification in condition monitoring of critical infrastructure using complex timeseries dependency modeling","year":2025,"lang":"en","type":"article","venue":"Measurement Energy","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bruce Power (Canada)","funders":"","keywords":"Noise (video); Dependency (UML); Component (thermodynamics); Calibration; Time series; Observational error; Stylized fact; Condition-based maintenance; Time horizon; Asset (computer security)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085754,0.000188651,0.000274311,0.0002516248,0.00008738151,0.00004510365,0.000121853,0.0001391437,0.00001402043],"category_scores_gemma":[0.0001220957,0.0001955513,0.00006332566,0.0003381578,0.00002701238,0.0001387858,0.00001403426,0.000158147,6.765334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006799729,"about_ca_system_score_gemma":0.00007847532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004139152,"about_ca_topic_score_gemma":0.0002459488,"domain_scores_codex":[0.9982665,0.0001017079,0.0005120668,0.0002149365,0.0006776701,0.000227112],"domain_scores_gemma":[0.9992365,0.00001394885,0.00004081069,0.0002201656,0.0004461193,0.00004242614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001304535,0.00001892457,0.0003865374,0.0001354347,0.00003241691,6.139301e-7,0.00005868013,0.5494341,0.4481889,0.0008371393,0.00003725219,0.0008570037],"study_design_scores_gemma":[0.0005394033,0.00001556309,0.002403935,0.0004563128,0.00003399169,0.000002020104,0.0006757819,0.920309,0.07448278,0.0005971186,0.0002873167,0.0001967815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7052212,0.00431428,0.2828232,0.0002032881,0.003706243,0.0005167904,0.00002169977,0.0004637155,0.002729604],"genre_scores_gemma":[0.9995508,0.00001974615,0.0002698782,0.00001220527,0.00007453709,0.00004209729,0.000006918197,0.00001772045,0.000006101908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3737061,"threshold_uncertainty_score":0.797435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04539531839411859,"score_gpt":0.2745856208556263,"score_spread":0.2291903024615077,"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."}}