{"id":"W2164053975","doi":"10.1109/tim.2007.909996","title":"Wavelet Distance Measure for Person Identification Using Electrocardiograms","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":360,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Biometrics; Artificial intelligence; Pattern recognition (psychology); Computer science; Wavelet transform; Modality (human–computer interaction); Measure (data warehouse); Identification (biology); Fingerprint (computing); Wavelet; Thumb; Residual; Speech recognition; Data mining; Medicine","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":[0.001797584,0.0004939319,0.0008404616,0.002024353,0.0002367237,0.000793057,0.000615468,0.0007520159,0.0009866172],"category_scores_gemma":[0.008234922,0.000128858,0.0003312133,0.001456449,0.0004120823,0.001537614,0.0006357351,0.0005076862,0.0005986428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003257052,"about_ca_system_score_gemma":0.0002801442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005514327,"about_ca_topic_score_gemma":0.0003653037,"domain_scores_codex":[0.9973505,0.0005209359,0.0002010666,0.0003025541,0.001560324,0.00006459194],"domain_scores_gemma":[0.997263,0.001375313,0.0003193006,0.0002598906,0.0006974224,0.00008515979],"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":[0.001380782,0.0003490212,0.01616376,0.0004862103,0.00029004,0.0002163942,0.0001968184,0.03197403,0.05850892,0.006205103,0.001947443,0.8822814],"study_design_scores_gemma":[0.0001124454,0.002203048,0.05467644,0.00009285451,0.0001939628,0.00189826,0.0002883348,0.8593989,0.0660051,0.006392119,0.008552005,0.0001865692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2697694,0.00341586,0.7226683,0.0002299124,0.0002994665,0.0001210729,0.0002650008,0.0006674056,0.002563627],"genre_scores_gemma":[0.7434025,0.001192281,0.2532774,0.00006280939,0.0001542048,0.0001009604,0.0004387298,0.00005632674,0.001314839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002024353,"threshold_uncertainty_score":0.009506643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09714402436983917,"score_gpt":0.2936976529173412,"score_spread":0.1965536285475021,"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."}}