{"id":"W4386920288","doi":"10.1109/sas58821.2023.10254072","title":"A Novel Approach for IMU Denoising using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Inertial measurement unit; Noise reduction; Artificial intelligence; Computer vision; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007982213,0.0008423582,0.001106284,0.0009032686,0.0004024338,0.0006188006,0.001230086,0.001043432,0.001100479],"category_scores_gemma":[0.001827819,0.0004345802,0.0009958465,0.0008669127,0.0004207711,0.0008223049,0.0008887762,0.001139626,0.001058084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004198196,"about_ca_system_score_gemma":0.0006309863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002229051,"about_ca_topic_score_gemma":0.002500607,"domain_scores_codex":[0.9993618,0.0001013263,0.00003644153,0.0001617433,0.0002787516,0.00005988246],"domain_scores_gemma":[0.9995556,0.0001024323,0.00005391955,0.00007813574,0.0001934823,0.00001622651],"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":[0.0001251907,0.0001111237,0.001883265,0.0001882125,0.0001446676,0.0001474555,0.0001086008,0.2483212,0.0369985,0.01029422,0.003982106,0.6976954],"study_design_scores_gemma":[0.000004485249,0.0000332433,0.0005063239,0.000009596953,0.00001252185,0.00005600253,0.000008466083,0.9886276,0.005903663,0.001958581,0.002869404,0.00001012297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002674973,0.0001372377,0.9962864,0.00004431259,0.00005685433,0.00001487786,0.00001882482,0.0003266995,0.0004397435],"genre_scores_gemma":[0.2041353,0.0004216195,0.7899395,0.0002004478,0.000231627,0.0001457199,0.0003008448,0.0001679095,0.004457005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002229051,"threshold_uncertainty_score":0.004432142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04338130285635956,"score_gpt":0.2416659157336004,"score_spread":0.1982846128772409,"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."}}