{"id":"W2031252452","doi":"10.1016/j.ultras.2006.05.214","title":"Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification","year":2006,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Wavelet; Artificial intelligence; Computer science; Feature extraction; Basis (linear algebra); Invariant (physics); Feature selection; Probabilistic logic; Wavelet transform; Mathematics","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":[0.000400932,0.0002858609,0.0005161728,0.0006621545,0.0002163136,0.000388942,0.0003126173,0.0003419694,0.00134314],"category_scores_gemma":[0.00110405,0.0001752872,0.0003900972,0.0008096487,0.0001701746,0.0003957642,0.0002892648,0.0003901387,0.000563235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000131024,"about_ca_system_score_gemma":0.0003768313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001157207,"about_ca_topic_score_gemma":0.001077437,"domain_scores_codex":[0.9998308,0.00003177636,0.00001367344,0.00002969343,0.00007381512,0.00002015372],"domain_scores_gemma":[0.9997031,0.0001038709,0.00002368861,0.00003615501,0.0001203227,0.00001270888],"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":[0.0002322116,0.00009801523,0.001190413,0.00008342302,0.00002562292,0.00005836225,0.00004680518,0.01133486,0.1547948,0.001436845,0.001636925,0.8290616],"study_design_scores_gemma":[0.00003022705,0.0002055205,0.01132271,0.00001510846,0.00008989404,0.0002983124,0.00006623291,0.8894935,0.09258274,0.00208205,0.003782254,0.000031458],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06559662,0.0003842775,0.9325281,0.00008691453,0.00005291483,0.00003293208,0.0001202257,0.0004754983,0.0007225909],"genre_scores_gemma":[0.5142634,0.0006576063,0.4804628,0.00005148428,0.00007505972,0.0001182729,0.0005850523,0.000126498,0.003659991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00134314,"threshold_uncertainty_score":0.004493237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324294223632403,"score_gpt":0.2639166455425065,"score_spread":0.2506737033061825,"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."}}