{"id":"W4390446791","doi":"10.22489/cinc.2023.370","title":"Synthetic Seismocardiography Signal Generation by a Generative Adversarial Network","year":2023,"lang":"en","type":"article","venue":"Computing in cardiology","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Adversarial system; Computer science; Generative grammar; Generative adversarial network; SIGNAL (programming language); Artificial intelligence; Deep learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0006375457,0.0005658582,0.0002707508,0.0002229571,0.0001024352,0.0002764756,0.000460996,0.0004476456,0.001130019],"category_scores_gemma":[0.001554748,0.0002120228,0.0004177753,0.0001641742,0.0004419673,0.0002309247,0.0005477309,0.000788754,0.0002384794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00028613,"about_ca_system_score_gemma":0.0002725153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001142666,"about_ca_topic_score_gemma":0.001262653,"domain_scores_codex":[0.9998295,0.00006130117,0.000005438599,0.00004269296,0.00004349035,0.00001751441],"domain_scores_gemma":[0.9995052,0.0003400516,0.00004108835,0.00004653577,0.00004846648,0.00001869394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000084338,0.0000287673,0.0009365894,0.00003343992,0.00002655679,0.0001008732,0.00003646196,0.9631951,0.008125124,0.003883657,0.001077745,0.02247146],"study_design_scores_gemma":[0.000002929803,0.00001431555,0.0001370766,0.000003155215,0.000002664191,0.00002061178,0.000002093147,0.9974432,0.001295523,0.0008272189,0.0002481027,0.000003073029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08110857,0.0002003478,0.9145323,0.000305732,0.0001073962,0.00008999176,0.0002563103,0.0006098931,0.002789396],"genre_scores_gemma":[0.896894,0.0001562375,0.09927679,0.0001493632,0.00004969441,0.0001083907,0.000452136,0.00007446339,0.002838949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001142666,"threshold_uncertainty_score":0.003780305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540661563995599,"score_gpt":0.2234233913927912,"score_spread":0.2080167757528352,"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."}}