{"id":"W3161817942","doi":"10.1109/tsp.2023.3262179","title":"Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Replicate; Calibration; Computer science; Constant (computer programming); Task (project management); Sampling (signal processing); SIGNAL (programming language); Inertial frame of reference; Stochastic modelling; Inertial navigation system; Noise (video); Algorithm; Statistics; Artificial intelligence; Mathematics; Engineering; Computer vision","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":[0.0001824064,0.0001901876,0.0001795715,0.0003181972,0.0002078024,0.00007875782,0.00006926502,0.0001501635,0.00001700766],"category_scores_gemma":[0.000004632827,0.0001986523,0.00007966815,0.000713724,0.00002738123,0.000349631,5.698294e-7,0.000251054,0.00001125567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008571772,"about_ca_system_score_gemma":0.00002902901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001725572,"about_ca_topic_score_gemma":0.00003082032,"domain_scores_codex":[0.9988734,0.00002642133,0.00035611,0.0002591956,0.0001683595,0.0003165877],"domain_scores_gemma":[0.9996977,0.00008068121,0.00003766841,0.00007946132,0.00004848873,0.0000559799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007395954,0.00003620296,0.000009852888,0.0001301878,0.00001249198,0.000001901544,0.0003965874,0.7267919,0.208857,0.000002687193,0.000007581964,0.06367968],"study_design_scores_gemma":[0.000550068,0.00002748833,0.00005031616,0.00008919054,0.00001558963,0.000002316365,0.0001448902,0.7299614,0.2689135,0.00002156382,0.00004989468,0.0001737426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1739368,0.00003387402,0.8249081,0.00004273781,0.0001270323,0.0002965463,0.00001984513,0.0006073811,0.0000277613],"genre_scores_gemma":[0.991651,0.000006914801,0.007891258,0.00001962105,0.0001231052,0.0001202218,0.00004612725,0.00005996608,0.00008179091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8177142,"threshold_uncertainty_score":0.8100804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08411182869630203,"score_gpt":0.2826700023894771,"score_spread":0.1985581736931751,"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."}}