{"id":"W4380032401","doi":"10.1109/tim.2023.3284947","title":"Intelligent Suppression of Non-Maneuvering Magnetic Interference of Aeromagnetic UAV","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Magnetometer; Interference (communication); Magnetic field; Magnetic dipole; Aeromagnetic survey; Magnetic survey; Fluxgate compass; Electromagnetic interference; Electronic engineering; Acoustics; Computer science; Engineering; Magnetic anomaly; Control theory (sociology); Electrical engineering; Physics; Geophysics; Artificial intelligence; Channel (broadcasting)","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":[0.0003216051,0.000682339,0.0004128306,0.0003464238,0.0002199999,0.00035178,0.0004084955,0.0002615786,0.0003286967],"category_scores_gemma":[0.0007533173,0.0002176407,0.0002916196,0.0002865023,0.0003016099,0.0003521021,0.0003702202,0.0002338846,0.0001442363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451571,"about_ca_system_score_gemma":0.0003524358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210444,"about_ca_topic_score_gemma":0.001753225,"domain_scores_codex":[0.9997715,0.00003793929,0.000008330231,0.0000417037,0.0001106267,0.00002990394],"domain_scores_gemma":[0.999648,0.0001104887,0.00008154344,0.00003793786,0.0001019047,0.00002024352],"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":[0.0002262174,0.00008941406,0.004180237,0.000257304,0.00006264506,0.0002479796,0.0002499387,0.4381656,0.4087424,0.004012224,0.0008686158,0.1428974],"study_design_scores_gemma":[0.00001928097,0.0002028971,0.002108669,0.000008444982,0.00002894347,0.0001029189,0.00005931429,0.9286812,0.06600164,0.0008538225,0.001914467,0.00001846391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1731624,0.0003182163,0.821112,0.00007493297,0.00003224708,0.00002913281,0.00002172848,0.0006933522,0.004556063],"genre_scores_gemma":[0.9138234,0.0001230491,0.08495107,0.00003910093,0.00001024764,0.0000345691,0.00004219924,0.00006629952,0.0009101512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001210444,"threshold_uncertainty_score":0.002504289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262926950201025,"score_gpt":0.2395590018414972,"score_spread":0.2132663068213947,"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."}}