{"id":"W4410782640","doi":"10.1007/s10845-025-02627-z","title":"A Time-Frequency Domain Feature Fusion Model Based on Unimodal Binomial Distribution for Fault Severity Classification","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Negative binomial distribution; Binomial distribution; Fault (geology); Fusion; Time domain; Distribution (mathematics); Computer science; Domain (mathematical analysis); Pattern recognition (psychology); Frequency domain; Artificial intelligence; Mathematics; Statistics; Geology; Poisson distribution; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009442451,0.0004052862,0.0009706412,0.0007755953,0.0003253611,0.0007059647,0.0008806965,0.0006231501,0.001054207],"category_scores_gemma":[0.001481323,0.0002097507,0.0006934343,0.0008350195,0.0002763315,0.001101788,0.000455017,0.0007458328,0.0004723922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005133778,"about_ca_system_score_gemma":0.0006464386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005664051,"about_ca_topic_score_gemma":0.003642045,"domain_scores_codex":[0.9995793,0.00006950351,0.00002979285,0.0001218345,0.0001392935,0.00006028719],"domain_scores_gemma":[0.9995778,0.0001673349,0.00003637305,0.00003086617,0.0001693372,0.00001832786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000524056,0.0002639274,0.003898311,0.000138125,0.0002112744,0.0001723571,0.0001122451,0.4197752,0.02155021,0.008561428,0.002555625,0.5422372],"study_design_scores_gemma":[0.000003796447,0.00003722628,0.0006711863,0.000003551545,0.00001901182,0.00003180248,0.000005265911,0.9970807,0.0008170389,0.001121358,0.0002017301,0.000007307437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02878981,0.0005232228,0.9692487,0.0001231395,0.00007226278,0.00003321066,0.0001021306,0.0003963635,0.0007111664],"genre_scores_gemma":[0.9021208,0.0006120282,0.09441008,0.0001244709,0.00007982123,0.0001084872,0.0003547854,0.00003330117,0.002156197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005664051,"threshold_uncertainty_score":0.01126218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034496808108285,"score_gpt":0.2714596471729466,"score_spread":0.2611146790918637,"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."}}