{"id":"W1627791142","doi":"","title":"Suppression of motion artifacts in optical action potential records by independent component analysis","year":2012,"lang":"en","type":"article","venue":"Computing in Cardiology","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Independent component analysis; Artifact (error); Computer science; Component (thermodynamics); Signal processing; Motion (physics); Artificial intelligence; Motion analysis; SIGNAL (programming language); Pattern recognition (psychology); Blind signal separation; Computer vision; Telecommunications; Physics","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":[0.0005469659,0.000114344,0.0006803873,0.0003197618,0.00002761153,0.000002008457,0.00004226816,0.0002411606,0.0000147191],"category_scores_gemma":[0.00007704391,0.0001068188,0.0002256855,0.0003520614,0.00007318137,0.00004394311,0.00006852323,0.0003910169,0.000006894531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001237297,"about_ca_system_score_gemma":0.00002066927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005461167,"about_ca_topic_score_gemma":0.000003768388,"domain_scores_codex":[0.9985715,0.0003362788,0.0003743984,0.0002336355,0.0001247255,0.0003594755],"domain_scores_gemma":[0.9994738,0.0001219311,0.0001011403,0.0001908855,0.00003820091,0.00007409401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000525112,0.00020713,0.7649102,0.00003944201,0.0003832169,0.00009227893,0.0001614928,0.05647788,0.166783,0.00005273375,0.0002075077,0.01015998],"study_design_scores_gemma":[0.0008105952,0.0001192986,0.9813378,0.00002481963,0.0002184863,0.0001871578,0.00005236304,0.009351317,0.007745902,0.00003007282,0.00003425766,0.00008796524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891556,0.000222581,0.009479838,0.00009996874,0.0006016527,0.0001483231,0.000002220095,0.00001824103,0.0002716131],"genre_scores_gemma":[0.9993025,0.00003875856,0.0002321007,0.00003632848,0.0002646762,0.000003612053,0.0001057547,0.000007680865,0.000008605342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2164275,"threshold_uncertainty_score":0.4355945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682279048203902,"score_gpt":0.2930994140979342,"score_spread":0.2762766236158952,"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."}}