{"id":"W4292873517","doi":"10.1109/memea54994.2022.9856579","title":"A Toolkit for Motion Artifact Signal Generation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artifact (error); Computer science; SIGNAL (programming language); Artificial intelligence; Motion (physics); Artificial neural network; Autoregressive model; Computer vision; Hidden Markov model; Pattern recognition (psychology); Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008063332,0.0001469203,0.0001911628,0.0001460592,0.0004864538,0.00004657521,0.0001911454,0.00006254758,0.001083359],"category_scores_gemma":[0.00007333891,0.000140167,0.0001352826,0.0002081223,0.00004273474,0.00006000879,0.0000486217,0.0002633267,0.00001929916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000274959,"about_ca_system_score_gemma":0.00007147064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002534604,"about_ca_topic_score_gemma":0.00000599873,"domain_scores_codex":[0.9972805,0.00005886043,0.0003788079,0.0004386572,0.001656823,0.0001863131],"domain_scores_gemma":[0.9991872,0.00006569541,0.0001264661,0.0002050262,0.0002001828,0.0002154753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00063152,0.004223892,0.0282857,0.0001330503,0.001475853,0.00001660233,0.0002633425,0.004884822,0.4578103,0.00419138,0.02645981,0.4716237],"study_design_scores_gemma":[0.007470962,0.001613149,0.003860512,0.0001400466,0.0007924575,0.0001352668,0.0003877846,0.2162305,0.03968723,0.0009408434,0.7278794,0.0008618821],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5451673,0.0004796594,0.3438233,0.08644235,0.00426304,0.005259014,0.000547495,0.0004413895,0.01357645],"genre_scores_gemma":[0.9907001,0.00007130821,0.0005474061,0.001631498,0.002051631,0.002681181,0.0005095673,0.00002573129,0.001781559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7014196,"threshold_uncertainty_score":0.9998298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0686610586178302,"score_gpt":0.3312239678588718,"score_spread":0.2625629092410416,"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."}}