{"id":"W2977528371","doi":"10.22215/etd/2019-13546","title":"Characterization and compensation of magnetic interference resulting from unmanned aircraft systems","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Servomechanism; Interference (communication); Rotor (electric); Compensation (psychology); Magnetometer; Engineering; Automotive engineering; Computer science; Electrical engineering; Aerospace engineering; Control theory (sociology); Magnetic field; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00005086991,0.0001622046,0.0002571329,0.0001004664,0.00002013036,0.00003784151,0.00006058815,0.0002069458,0.00003808235],"category_scores_gemma":[0.00001647249,0.0001659572,0.00002485788,0.0000813638,0.000006870106,0.0001120073,0.000005569978,0.0001228958,0.00002010123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002898333,"about_ca_system_score_gemma":0.000007820227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004289504,"about_ca_topic_score_gemma":0.00007716804,"domain_scores_codex":[0.9992027,0.00002536098,0.0003914347,0.0001625082,0.0001224924,0.00009545171],"domain_scores_gemma":[0.9995729,0.00004655648,0.0001251164,0.0001276815,0.0001046705,0.00002304939],"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":[0.00005874662,0.000006935901,0.0003738027,0.0007299064,0.0000263895,4.208378e-7,0.001402466,0.001195867,0.9879971,0.0001903495,0.00001578848,0.008002189],"study_design_scores_gemma":[0.000820309,0.0001585939,0.1068529,0.002167735,0.0001449853,0.000001541844,0.001714809,0.6749628,0.2122504,0.0000316375,0.0002166626,0.000677493],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925511,0.0002604224,0.002715552,0.000003044485,0.001259203,0.0003272663,0.00007061216,0.0001121565,0.002700664],"genre_scores_gemma":[0.990223,0.0001094966,0.0000776279,0.00000266734,0.00008179159,0.000008260503,0.007824839,0.00003284615,0.001639499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7757467,"threshold_uncertainty_score":0.6767538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007879473539066208,"score_gpt":0.2063043388818402,"score_spread":0.198424865342774,"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."}}