{"id":"W2796142916","doi":"10.3390/s18041067","title":"Artifact Noise Removal Techniques on Seismocardiogram Using Two Tri-Axial Accelerometers","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Accelerometer; Acceleration; Noise (video); Computer science; Digital signal processing; Artifact (error); Signal processing; Data acquisition; Digital filter; Filter (signal processing); Electronic engineering; Computer vision; Engineering; Computer hardware; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001737822,0.0002963782,0.0002814306,0.0003182653,0.0001191593,0.00008314702,0.0001843969,0.0001207361,0.0000295398],"category_scores_gemma":[0.00005454428,0.0003058813,0.0001700623,0.0004545127,0.000103338,0.000150684,0.00004655243,0.0002546313,0.000197889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000229808,"about_ca_system_score_gemma":0.00001336398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008774862,"about_ca_topic_score_gemma":0.000003458002,"domain_scores_codex":[0.9985709,0.00005021449,0.0002595356,0.0003096031,0.0002984146,0.0005113714],"domain_scores_gemma":[0.9992779,0.00007127147,0.00004181695,0.0004038163,0.00006179891,0.0001434162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005444939,0.00002449571,0.001258112,0.00003028947,0.0001208177,0.0001607052,0.0002185677,0.007221123,0.9434389,0.00002258234,0.0003215494,0.04712836],"study_design_scores_gemma":[0.0003180391,0.000171987,0.0004822257,0.0001028019,0.00003521036,0.00007004911,0.00005765131,0.003583384,0.9897652,0.0001304258,0.004828081,0.0004549217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986807,0.00002359837,0.002293445,0.000013941,0.001272438,0.0002677608,0.000008025478,0.001086297,0.008227433],"genre_scores_gemma":[0.9843441,0.0000122231,0.01364436,0.000042901,0.001787227,0.000008022643,0.000002843605,0.0001076463,0.00005071342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04667344,"threshold_uncertainty_score":0.9999393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0302093586883191,"score_gpt":0.2753834303683182,"score_spread":0.2451740716799991,"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."}}