{"id":"W4399390261","doi":"10.3390/s24113691","title":"Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Preprocessor; Artificial intelligence; Computational complexity theory; Feature extraction; Artificial neural network; Feature (linguistics); Data mining; Machine learning; Pattern recognition (psychology); Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003356361,0.000379287,0.0003125983,0.0002644765,0.0001700929,0.0002353026,0.0002185356,0.0002708788,0.0001913653],"category_scores_gemma":[0.0000316688,0.0003621723,0.0001972529,0.0007092686,0.00003796024,0.00009987598,0.00005705096,0.0006062349,0.0001429946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000261567,"about_ca_system_score_gemma":0.00001672457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003974692,"about_ca_topic_score_gemma":0.0001155784,"domain_scores_codex":[0.9981329,0.0001462957,0.0003473462,0.0005238793,0.0002931467,0.0005564017],"domain_scores_gemma":[0.9989047,0.000393983,0.00004060999,0.000436777,0.00005497664,0.0001689206],"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":[0.000005190092,0.0001146199,0.0002576186,0.0001080426,0.000217236,0.00006273259,0.000741748,0.9470289,0.003701941,0.006405088,0.0190405,0.02231633],"study_design_scores_gemma":[0.0001665875,0.0001008003,0.003195679,0.0009517367,0.0002348417,0.00004067068,0.0001783825,0.844912,0.07951225,0.002125442,0.06764,0.0009416407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6346661,0.00347633,0.3425482,0.001074647,0.003338368,0.001160996,0.00008026786,0.006899881,0.006755173],"genre_scores_gemma":[0.9799833,0.0007082665,0.01769614,0.0001475391,0.0005634223,0.0002913271,0.0001685175,0.0001343116,0.0003072039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3453172,"threshold_uncertainty_score":0.999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136553526056346,"score_gpt":0.3101570270907741,"score_spread":0.2965016744851395,"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."}}