{"id":"W4243154351","doi":"10.32920/ryerson.14651559","title":"Blind Source Separation in the Analysis of Electrocardiogram Pre-Shock Waveforms During Ventricular Fibrillation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ventricular fibrillation; Shock (circulatory); Blind signal separation; Waveform; Cardiology; Computer science; Internal medicine; Artificial intelligence; Medicine; Channel (broadcasting); Telecommunications","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":[0.0009963537,0.0002321884,0.0005259924,0.001110466,0.00007065287,0.0004570044,0.0008714673,0.0002825087,0.000009180391],"category_scores_gemma":[0.00004876101,0.0001754774,0.0005439587,0.003422576,0.00002185226,0.0003740377,0.0005410988,0.0004214557,0.000001056269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095947,"about_ca_system_score_gemma":0.0001362305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002187895,"about_ca_topic_score_gemma":0.000188667,"domain_scores_codex":[0.9974288,0.0003941441,0.0006590927,0.0006005174,0.0006834558,0.0002339666],"domain_scores_gemma":[0.9979936,0.00009234381,0.0004252777,0.001243546,0.0002119303,0.00003330497],"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":[0.00002917772,0.000132729,0.01141037,0.0001079654,0.001253715,0.00001992052,0.0116533,0.9653628,0.000884859,0.002703152,0.00005706492,0.006384946],"study_design_scores_gemma":[0.0002717617,0.00005388985,0.1342058,0.00005855408,0.0003673289,0.00001729481,0.000210144,0.8399207,0.02329611,0.001018614,0.0001968644,0.0003829224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4901274,0.000146787,0.5086982,0.0001520646,0.00002631358,0.0003776303,0.000001031844,0.0001085861,0.0003619258],"genre_scores_gemma":[0.9878758,0.00009704976,0.01158423,0.0000774049,0.00004278759,0.00005359807,0.0001739629,0.000009445731,0.00008570137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4977484,"threshold_uncertainty_score":0.7155762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329383027990841,"score_gpt":0.2908703408474163,"score_spread":0.2775765105675079,"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."}}