{"id":"W2273988938","doi":"10.4271/2005-01-2269","title":"Towards an Improved Knock Detection and Quantification using Wavelets and Entropy-based Noise Compensation","year":2005,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wavelet; Entropy (arrow of time); Computer science; Noise (video); Artificial intelligence; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001174976,0.0005203968,0.0005613031,0.0002792694,0.0005916227,0.0004508953,0.00068434,0.0004999322,0.00002662224],"category_scores_gemma":[0.0004240195,0.0004736788,0.0001501947,0.0006188534,0.0005447855,0.001477423,0.0002826393,0.0006602705,0.00001164831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000238963,"about_ca_system_score_gemma":0.0001095363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008587327,"about_ca_topic_score_gemma":0.008939344,"domain_scores_codex":[0.996385,0.0004049214,0.000764657,0.001260463,0.0005834131,0.0006015313],"domain_scores_gemma":[0.9978019,0.000291186,0.0002634208,0.001127887,0.0001749787,0.000340601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001805541,0.0001665729,0.00002331014,0.00002590643,0.000009858039,0.000007555709,0.00004384359,0.00004941547,0.8848734,0.004629601,0.00001552498,0.1099745],"study_design_scores_gemma":[0.001508986,0.001437355,0.9705681,0.0001113757,0.00007415674,0.0001733758,0.000031248,0.001049849,0.01718044,0.002826395,0.00422895,0.0008097223],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710135,0.0004072583,0.01856017,0.004669514,0.0002980067,0.001246,0.00001524334,0.002298212,0.001492103],"genre_scores_gemma":[0.8359046,0.0000609917,0.162575,0.001197045,0.000123231,0.00005118008,0.00001237483,0.00004460506,0.00003095415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9705448,"threshold_uncertainty_score":0.9997715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499774232694209,"score_gpt":0.2846063484123532,"score_spread":0.2596086060854111,"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."}}