{"id":"W4377832601","doi":"10.18280/ts.400227","title":"Multi-Grained Deep Cascade Learning for ECG Biometric Recognition","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province","keywords":"Discriminative model; Biometrics; Computer science; Artificial intelligence; Pattern recognition (psychology); Deep learning; Sparse approximation; Cascade; Feature learning; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000566984,0.0007032258,0.0006739361,0.0005097397,0.0002801701,0.0003360855,0.001124358,0.0006815932,0.002535109],"category_scores_gemma":[0.0008589317,0.0003600811,0.0005982361,0.000487491,0.0002378796,0.0007318022,0.0007141107,0.0009206109,0.0005952998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006493608,"about_ca_system_score_gemma":0.0006720742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006596299,"about_ca_topic_score_gemma":0.00950405,"domain_scores_codex":[0.9996923,0.00004415855,0.00001691775,0.00009268992,0.0001054615,0.00004849544],"domain_scores_gemma":[0.9997876,0.00006010358,0.00002431581,0.000034837,0.00007301442,0.00002005367],"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":[0.000370723,0.0003136,0.002224466,0.0001190375,0.0001306136,0.0002244393,0.00006666345,0.379048,0.04140153,0.006123336,0.005711127,0.5642664],"study_design_scores_gemma":[0.000003307032,0.00003273775,0.0002203931,0.000002326619,0.000007958953,0.00001911689,0.000001931655,0.9967283,0.002136161,0.0005978253,0.0002460054,0.000003830879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04095208,0.001089123,0.9533979,0.0002214037,0.00009161289,0.00007850922,0.0001499831,0.001619865,0.002399521],"genre_scores_gemma":[0.7919225,0.0006914226,0.2005598,0.0002253882,0.00007738136,0.0001113199,0.0005228143,0.00006376542,0.005825636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006596299,"threshold_uncertainty_score":0.01311582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06909177032070528,"score_gpt":0.3183924402025688,"score_spread":0.2493006698818636,"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."}}