{"id":"W4413863034","doi":"10.35490/ec3.2025.311","title":"Graph Deviation Network for Fault Detection and Diagnosis Using Building Automation System Data","year":2025,"lang":"en","type":"article","venue":"Computing in construction","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Automation; Graph; Fault detection and isolation; Fault (geology); Data mining; Real-time computing; Reliability engineering; Artificial intelligence; Theoretical computer science; Engineering; Geology; Seismology","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":[0.0006727224,0.0001229196,0.0001907242,0.0003086001,0.0002616814,0.0001035091,0.00007087843,0.0001731078,3.679246e-7],"category_scores_gemma":[0.00009937227,0.000143304,0.00002817016,0.0006181989,0.00001825438,0.0002423437,0.00004437595,0.0001286852,3.620292e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002500635,"about_ca_system_score_gemma":0.00001705309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000984546,"about_ca_topic_score_gemma":0.00003951514,"domain_scores_codex":[0.998987,0.00006588379,0.0004233423,0.0002630776,0.00008198457,0.0001786689],"domain_scores_gemma":[0.9994071,0.000204294,0.0001070429,0.0001966627,0.00006347896,0.00002146665],"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":[0.00001602624,0.000003565292,0.008952804,0.0004146938,0.00003467713,3.66311e-7,0.00004157722,0.6714321,0.002364345,0.0008379698,0.0000737478,0.3158281],"study_design_scores_gemma":[0.0004536985,0.00001249098,0.003018565,0.0006679468,0.0000318246,0.00002586296,0.0001577649,0.9931284,0.001827071,0.0002026183,0.0003533626,0.0001204235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4633825,0.0001437146,0.5333044,0.000002964946,0.002582419,0.0003058701,0.000003890791,0.0002432578,0.00003100678],"genre_scores_gemma":[0.9828534,0.000009825669,0.01667909,0.000003634156,0.0004066593,0.00002004313,0.00001253923,0.00001405808,7.121988e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5194709,"threshold_uncertainty_score":0.5843766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309836135217591,"score_gpt":0.2794476059040237,"score_spread":0.2484639923822646,"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."}}