{"id":"W4406070225","doi":"10.1016/j.inffus.2025.102935","title":"Interpretable degradation tensor modeling through multi-scale and multi-level time-frequency feature fusion for machine health monitoring","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Scale (ratio); Fusion; Degradation (telecommunications); Feature (linguistics); Tensor (intrinsic definition); Artificial intelligence; Pattern recognition (psychology); Data mining; Mathematics; Physics; Telecommunications","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":[0.000782051,0.001065073,0.0005280285,0.0009331506,0.0002077743,0.0008028727,0.0005442312,0.0005492454,0.0007595754],"category_scores_gemma":[0.001935156,0.000278345,0.0009175679,0.0007405434,0.0004459399,0.001364729,0.0006698623,0.0009284858,0.0002644836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004360111,"about_ca_system_score_gemma":0.0005057722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034993,"about_ca_topic_score_gemma":0.001874984,"domain_scores_codex":[0.9996659,0.00006548632,0.00002848493,0.00008025969,0.0001229476,0.00003693948],"domain_scores_gemma":[0.9994395,0.0001414275,0.000147329,0.00008385622,0.0001565198,0.00003122438],"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":[0.0002304302,0.0001121214,0.004021205,0.0002564989,0.0001166872,0.000264446,0.0002131524,0.6190597,0.07291391,0.01186669,0.002086403,0.2888587],"study_design_scores_gemma":[0.000002638035,0.00003841181,0.0008099124,0.00000795366,0.00001638535,0.00004100932,0.00001679886,0.9895627,0.004951256,0.003819073,0.0007194382,0.00001446352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02031413,0.0002987129,0.9782438,0.0001261875,0.00003403967,0.00002381595,0.00009841857,0.0004039643,0.0004568836],"genre_scores_gemma":[0.7206076,0.0008424414,0.2759331,0.0001176465,0.0000966806,0.0001115855,0.0005666987,0.0001301005,0.001594221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002034993,"threshold_uncertainty_score":0.004135966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02384212500776184,"score_gpt":0.3091895435429453,"score_spread":0.2853474185351834,"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."}}