{"id":"W3027771045","doi":"10.1016/j.cjph.2020.05.009","title":"Weak fault feature extraction method based on compound tri-stable stochastic resonance","year":2020,"lang":"en","type":"article","venue":"Chinese Journal of Physics","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of Science and Technology Beijing; National Natural Science Foundation of China","keywords":"Stochastic resonance; Constraint (computer-aided design); Noise (video); Fault (geology); Gaussian; Feature extraction; Computer science; Gaussian noise; Feature (linguistics); Extraction (chemistry); Coupling (piping); Resonance (particle physics); Algorithm; Pattern recognition (psychology); Biological system; Artificial intelligence; Mathematics; Materials science; Physics","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":[0.0002351067,0.0006392271,0.0006647567,0.001363882,0.000365214,0.0004840357,0.0004209328,0.0004357716,0.001281935],"category_scores_gemma":[0.0007591908,0.0001781916,0.0005887218,0.0007113059,0.0001814185,0.0008185075,0.000434224,0.0004315856,0.0004153209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001642822,"about_ca_system_score_gemma":0.0003260933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009785215,"about_ca_topic_score_gemma":0.0009294899,"domain_scores_codex":[0.9998222,0.00001515688,0.00001766764,0.00004911626,0.0000703352,0.00002558547],"domain_scores_gemma":[0.9997396,0.00007867395,0.0000407775,0.00002177919,0.000100007,0.00001916111],"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":[0.0005647403,0.0001703118,0.01004305,0.000363862,0.0002011179,0.0008698907,0.0001875094,0.1019695,0.1667529,0.01048703,0.004792329,0.7035977],"study_design_scores_gemma":[0.00001372648,0.00009897455,0.004487548,0.000009472428,0.00007937084,0.0003059997,0.00003193528,0.9772041,0.01241876,0.003715845,0.001605011,0.00002927686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06790396,0.0003139749,0.9289051,0.00009752961,0.00008648657,0.00004562954,0.0001447085,0.0006711328,0.001831434],"genre_scores_gemma":[0.8939189,0.0003654923,0.1022763,0.00006539207,0.0001099024,0.00006190636,0.0004741596,0.00008266434,0.002645346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001363882,"threshold_uncertainty_score":0.004288554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420383904593078,"score_gpt":0.294576113062927,"score_spread":0.2803722740169962,"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."}}