{"id":"W4385604414","doi":"10.2139/ssrn.4524798","title":"PSC-Net: Integration of Convolutional Neural Networks and Transformers for Physiological Signal Classification","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Convolutional neural network; Computer science; Transformer; Net (polyhedron); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Engineering; Mathematics; Electrical engineering; Voltage","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001078316,0.0002130905,0.0004818714,0.000185532,0.0001328079,0.00002503482,0.00009577208,0.0003266273,0.000006947942],"category_scores_gemma":[0.00007032252,0.0001675857,0.0003782902,0.0001312381,0.0001120768,0.0000484207,0.00002642202,0.002568759,7.53194e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004396267,"about_ca_system_score_gemma":0.0008345312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004307761,"about_ca_topic_score_gemma":0.00006036007,"domain_scores_codex":[0.9979971,0.0000752988,0.0005010652,0.0003080709,0.0002527787,0.0008656583],"domain_scores_gemma":[0.9991239,0.0001028188,0.0003293915,0.00009531737,0.0002527796,0.00009577809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005578061,0.001128025,0.02953258,0.001158884,0.008806607,0.00001367227,0.0006879265,0.05005487,0.10723,0.03959128,0.001285719,0.7549324],"study_design_scores_gemma":[0.001756509,0.001519312,0.02936188,0.0003478119,0.001163861,0.00014146,0.001757708,0.9100178,0.0002426001,0.05333518,0.00004308831,0.0003128187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4746195,0.002770769,0.5198715,0.001812605,0.0004018591,0.0004228941,0.00002248437,0.00005248327,0.00002586971],"genre_scores_gemma":[0.9916669,0.006118399,0.0002711227,0.00002314706,0.001156998,0.00003601949,0.0002797383,0.00002702096,0.0004207241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8599629,"threshold_uncertainty_score":0.9997324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0547883299765588,"score_gpt":0.317555189139536,"score_spread":0.2627668591629772,"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."}}