{"id":"W2798445441","doi":"10.1049/iet-spr.2018.5076","title":"Block sparse multi‐lead ECG compression exploiting between‐lead collaboration","year":2018,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Queen's University","funders":"","keywords":"Kernel (algebra); Computer science; Compression ratio; Wavelet; Estimator; Algorithm; Block (permutation group theory); Compression (physics); Compressed sensing; Discrete cosine transform; Pattern recognition (psychology); Gaussian; Daubechies wavelet; Artificial intelligence; Mathematics; Wavelet transform; Discrete wavelet transform; Statistics; Image (mathematics); Discrete mathematics","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":[0.0006217695,0.000231721,0.000250424,0.0001922932,0.0006043222,0.0007796935,0.0006765417,0.0001555616,0.00001237873],"category_scores_gemma":[0.00005547549,0.000225366,0.00004720894,0.0008485042,0.0001301058,0.001923441,0.0002259084,0.0002646115,0.00009137371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006470676,"about_ca_system_score_gemma":0.0002149161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001351845,"about_ca_topic_score_gemma":0.00001246577,"domain_scores_codex":[0.9979504,0.0001742064,0.0004583501,0.000559137,0.0004908971,0.0003669562],"domain_scores_gemma":[0.9985169,0.00009563279,0.000349542,0.0003361779,0.0005947628,0.0001069453],"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":[0.00004582342,0.0003275531,0.004582341,0.0001757503,0.00003272776,0.00002166952,0.01896748,0.0005217177,0.2407982,0.0012349,0.004966492,0.7283253],"study_design_scores_gemma":[0.0004813774,0.0002105934,0.0005145734,0.0003779657,0.00001231617,0.000009563384,0.0003463772,0.6248396,0.3689083,0.001528127,0.002340535,0.0004306182],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07378044,0.0001137928,0.9227867,0.0006695961,0.00009681158,0.0002459468,0.00000286004,0.0009302673,0.001373578],"genre_scores_gemma":[0.7536656,0.000002100076,0.2454791,0.0004205618,0.0002672655,0.00001902184,0.000007592098,0.00002049625,0.0001182267],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7278947,"threshold_uncertainty_score":0.9190157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05522897924781725,"score_gpt":0.3275315428156317,"score_spread":0.2723025635678145,"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."}}