{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001073475,0.00009533481,0.000114419,0.000170299,0.00003647447,0.000007193452,0.00004403936,0.00003768781,0.00007539205],"category_scores_gemma":[0.000002108693,0.00009539979,0.00003701153,0.0002037909,0.00003024395,0.00006538598,8.263559e-7,0.00007207623,0.00001014496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004904648,"about_ca_system_score_gemma":0.000007955543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003925301,"about_ca_topic_score_gemma":0.00002506758,"domain_scores_codex":[0.9992377,0.00001731314,0.0002823889,0.00009971229,0.0002593056,0.0001036171],"domain_scores_gemma":[0.9997566,0.00001462066,0.00003483005,0.00009092939,0.00006223989,0.00004076149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000425017,0.00004103985,0.00003818431,0.000165182,0.00002008948,4.233584e-7,0.0009597431,0.03613991,0.8139262,0.000009519041,0.00003284919,0.1486244],"study_design_scores_gemma":[0.0004043479,0.0002986237,0.002710984,0.0002040747,0.00003025874,0.000001322239,0.0003886953,0.02989886,0.9659244,0.00001890603,0.00003144053,0.00008806685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225233,0.00003241212,0.07618375,0.00001680764,0.0005943325,0.0002107233,0.0000097973,0.0000709968,0.0003578863],"genre_scores_gemma":[0.9994585,0.0002809324,0.0001833797,0.000005636823,0.00000731599,0.00002328112,0.000003303636,0.00001145772,0.00002619806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1519982,"threshold_uncertainty_score":0.389029,"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."}}