{"id":"W3175723730","doi":"10.1177/00202940211021876","title":"Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration","year":2021,"lang":"en","type":"article","venue":"Measurement and Control","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Science Foundation of Heilongjiang Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Magnetometer; Heading (navigation); Inertial measurement unit; Interference (communication); Kalman filter; Control theory (sociology); Calibration; Filter (signal processing); Computer science; Gyroscope; Magnetic field; Acoustics; Physics; Engineering; Channel (broadcasting); Computer vision; Artificial intelligence; Aerospace engineering; Telecommunications","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":[0.0001929003,0.00009054936,0.0001452731,0.0000536791,0.00003177143,0.00003294227,0.00002495937,0.00004260972,0.00001446929],"category_scores_gemma":[0.00004,0.00008167163,0.00002626566,0.0001025259,0.00001229572,0.00007875849,0.000002456497,0.00006657706,0.00000228768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001041894,"about_ca_system_score_gemma":0.00001985974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001650702,"about_ca_topic_score_gemma":0.00001149323,"domain_scores_codex":[0.9993309,0.00005703852,0.0002117934,0.000102406,0.0002047657,0.00009306182],"domain_scores_gemma":[0.9996992,0.00002483535,0.0000336218,0.00008406227,0.0001271758,0.00003114848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001367756,0.00002896462,0.00101148,0.0002540757,0.00002666828,0.000003670715,0.0001158973,0.06240811,0.9183909,0.0004095752,0.00005144174,0.01716239],"study_design_scores_gemma":[0.0008816858,0.0000763549,0.001068587,0.0002526545,0.00002727594,7.068235e-7,0.00005080102,0.8237423,0.1738026,0.000005755944,0.0000193315,0.0000719622],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6741506,0.0002925113,0.3235141,0.0001444392,0.0002851034,0.0002745058,0.0000235293,0.0001507081,0.001164484],"genre_scores_gemma":[0.9997138,0.000002329077,0.0001672332,0.00004736024,0.00002155493,0.00001286117,0.00002017583,0.000008759999,0.000005948707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7613342,"threshold_uncertainty_score":0.3330472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160793788356943,"score_gpt":0.1922878690503808,"score_spread":0.1762084902146865,"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."}}