{"id":"W4416530330","doi":"10.1088/1361-6501/ae228a","title":"A dynamic spatial-temporal graph transformer with multi-frequency attention for remaining useful life prediction","year":2025,"lang":"","type":"article","venue":"Measurement Science and Technology","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Transformer; Modular design; Adjacency list; Fuse (electrical); Graph; Attention network; Convolution (computer science); Convolutional neural network","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":[0.0003544615,0.0006646041,0.0004959324,0.0009350691,0.0001971527,0.0004553239,0.0009696365,0.0005513805,0.001481215],"category_scores_gemma":[0.001117479,0.000216959,0.0006569739,0.0006955728,0.0002966769,0.0009734927,0.0004582695,0.0007708424,0.0002797126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007259394,"about_ca_system_score_gemma":0.0006317662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01648998,"about_ca_topic_score_gemma":0.01502162,"domain_scores_codex":[0.9998589,0.0000212485,0.000006633411,0.00004975395,0.00003823538,0.00002523825],"domain_scores_gemma":[0.9996537,0.0001441443,0.00004831722,0.00003015816,0.00009517298,0.00002853137],"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":[0.0001721711,0.0001142741,0.003868251,0.00005391533,0.0000680242,0.0001524188,0.00003485035,0.8235171,0.008752664,0.003644689,0.003078046,0.1565436],"study_design_scores_gemma":[0.000001139465,0.000006775957,0.000179459,9.758899e-7,0.000003822713,0.000008575682,0.000001406798,0.9986755,0.0003932072,0.0006388804,0.00008859543,0.000001606615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1094977,0.0008881407,0.8844312,0.0005114676,0.0001012654,0.00004183138,0.0004220308,0.001897695,0.00220855],"genre_scores_gemma":[0.9651008,0.0002200443,0.03263381,0.000108584,0.00003479313,0.0000236398,0.0003112235,0.00004700018,0.001520176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01648998,"threshold_uncertainty_score":0.03278798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01764281477811892,"score_gpt":0.2694198156882925,"score_spread":0.2517770009101736,"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."}}