{"id":"W4407693069","doi":"10.1109/tim.2025.3542885","title":"Dual-Contrastive Multiview Graph Attention Network for Industrial Fault Diagnosis Under Domain and Label Shift","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Dual (grammatical number); Graph; Graph theory; Theoretical computer science; Artificial intelligence; Mathematics","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":[0.0004680907,0.0001834554,0.0002056209,0.0001813127,0.0003210169,0.00008211992,0.0000230371,0.0001633736,0.00001226028],"category_scores_gemma":[0.000005987742,0.0001818685,0.00006874186,0.000251849,0.0000313011,0.0001460298,7.401105e-7,0.0001708065,0.000002190094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001747232,"about_ca_system_score_gemma":0.00002540142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005886726,"about_ca_topic_score_gemma":0.000212197,"domain_scores_codex":[0.9989162,0.00007520839,0.000345105,0.0002353864,0.0002318383,0.00019627],"domain_scores_gemma":[0.999639,0.00007777839,0.00005259026,0.00008512847,0.00007491676,0.00007059728],"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":[0.0007390261,0.0003376809,0.0008294768,0.0003152544,0.001093547,0.000001225907,0.0007515204,0.04402268,0.01181947,0.001308157,0.002499564,0.9362824],"study_design_scores_gemma":[0.2281843,0.008511256,0.07472134,0.01446639,0.00600168,0.00007709341,0.02325419,0.07215256,0.4610955,0.0217344,0.08164831,0.008153013],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.360099,0.0001969221,0.6352426,0.0001959956,0.002679592,0.001293579,0.00004967935,0.0001222764,0.0001203904],"genre_scores_gemma":[0.9984918,0.0002098436,0.0003611743,0.00009734556,0.0001019935,0.000687497,0.000005922034,0.00001608149,0.00002834837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9281294,"threshold_uncertainty_score":0.7416382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05278858484618111,"score_gpt":0.2676118612134171,"score_spread":0.214823276367236,"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."}}