{"id":"W2126734586","doi":"10.1155/2015/959380","title":"Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy Complexity","year":2015,"lang":"en","type":"article","venue":"Shock and Vibration","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Centre Technique des Industries Mécaniques; Natural Sciences and Engineering Research Council of Canada; Mitacs; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Sample entropy; Approximate entropy; Kurtosis; Entropy (arrow of time); Nonlinear system; Vibration; Computer science; Pattern recognition (psychology); Time domain; Fault detection and isolation; Signal processing; Artificial intelligence; Mathematics; Statistics; Acoustics; Actuator","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001773025,0.0007793991,0.0005789348,0.003965693,0.000247898,0.001067601,0.0004136202,0.0006510399,0.0005628848],"category_scores_gemma":[0.01015642,0.0001513138,0.0004096655,0.001638513,0.0007284217,0.002432838,0.0007543421,0.000510939,0.0001716787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309208,"about_ca_system_score_gemma":0.0003166276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006285611,"about_ca_topic_score_gemma":0.000592743,"domain_scores_codex":[0.999227,0.0002608125,0.00005158956,0.00008107597,0.0003392989,0.00004020608],"domain_scores_gemma":[0.9955174,0.003203507,0.0004554818,0.0003042273,0.0003916457,0.0001277408],"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":[0.001155241,0.0002719791,0.04725247,0.0007064682,0.0002682712,0.0004080822,0.000543352,0.3149371,0.03801177,0.02617578,0.001900137,0.5683694],"study_design_scores_gemma":[0.00001619685,0.0002387779,0.01948327,0.00003333063,0.00003760936,0.0002322589,0.0001171856,0.9598752,0.01244057,0.006765164,0.0006853578,0.00007508251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3141026,0.002789951,0.6774545,0.0002748315,0.00008680769,0.0001332097,0.000325443,0.0005792748,0.004253417],"genre_scores_gemma":[0.9286792,0.0008111118,0.06951886,0.00003317686,0.00008520737,0.0000788519,0.000267488,0.00004283965,0.0004831605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003965693,"threshold_uncertainty_score":0.009376764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05570386055088173,"score_gpt":0.2802372216798084,"score_spread":0.2245333611289266,"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."}}