{"id":"W4407281175","doi":"10.1016/j.inffus.2025.102999","title":"Multimodal fusion of spatial–temporal and frequency representations for enhanced ECG classification","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"National Science and Technology Council","keywords":"Computer science; Artificial intelligence; Fusion; Pattern recognition (psychology); Speech recognition","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":[],"consensus_categories":[],"category_scores_codex":[0.0001593904,0.0000694488,0.0001477848,0.0002624531,0.0001161594,0.00001686177,0.00003453308,0.00007596771,0.00002094569],"category_scores_gemma":[0.000238067,0.00006181644,0.00005993541,0.0002083498,0.00002950365,0.0002879559,0.00002184351,0.00005749437,0.000005345988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003382703,"about_ca_system_score_gemma":0.00005882551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005531872,"about_ca_topic_score_gemma":0.00002450964,"domain_scores_codex":[0.9992648,0.00001453388,0.0004119158,0.00009115824,0.0001405207,0.00007701511],"domain_scores_gemma":[0.9992189,0.00006243439,0.0001895065,0.0001729799,0.0003193042,0.0000368863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002786398,0.0001246082,0.1091121,0.0005928014,0.00006739137,2.04887e-7,0.001700853,0.00008864577,0.3329582,0.0009831956,0.0006277694,0.5534655],"study_design_scores_gemma":[0.004279532,0.000291119,0.5454196,0.0005830014,0.0002987895,0.000002442921,0.002538436,0.2619807,0.1804438,0.001124852,0.002829903,0.0002077721],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7161356,0.00003728014,0.2773515,0.0009840446,0.0001935038,0.0004658553,0.00001738967,0.00004984613,0.00476496],"genre_scores_gemma":[0.9886364,0.00006013427,0.01047812,0.00007502318,0.00003791714,0.00004218672,0.0003479921,0.000002947004,0.0003192466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5532578,"threshold_uncertainty_score":0.2520801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686809285165557,"score_gpt":0.318264640713212,"score_spread":0.3013965478615564,"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."}}