{"id":"W1983102968","doi":"10.1109/tmech.2013.2285224","title":"Magnetic Signature Attenuation of an Unmanned Aircraft System for Aeromagnetic Survey","year":2014,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Signature (topology); Attenuation; Magnetic anomaly; Servomotor; Computer science; Aeromagnetic survey; Genetic algorithm; Magnetic field; Magnetic survey; Aerospace engineering; Orientation (vector space); Physics; Engineering; Geophysics; Optics; Artificial intelligence; Mathematics","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":[0.00009192408,0.0002463296,0.0001656981,0.0002032581,0.0001551604,0.0002130553,0.0002875893,0.0001660522,0.0003916358],"category_scores_gemma":[0.0003211052,0.00007296844,0.00009568197,0.0001941782,0.0001440647,0.0002301968,0.0001984554,0.0001530169,0.0001405395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002606741,"about_ca_system_score_gemma":0.0002720596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354494,"about_ca_topic_score_gemma":0.002000747,"domain_scores_codex":[0.9998978,0.00002204542,0.000003126032,0.00001187342,0.00005225424,0.00001296686],"domain_scores_gemma":[0.9998848,0.00002504262,0.00003468593,0.00001573483,0.00003143916,0.000008255707],"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":[0.0003776315,0.00008438517,0.004872676,0.0001134207,0.0000341575,0.000380204,0.0001681626,0.3045501,0.4780571,0.00241998,0.0006885502,0.2082536],"study_design_scores_gemma":[0.00002446781,0.0004571082,0.004953196,0.000009829238,0.00002571257,0.0002965769,0.00004673004,0.885877,0.1041572,0.0004412448,0.003697287,0.00001368938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4390826,0.000215505,0.5519378,0.000156332,0.00003621404,0.0000395863,0.00002347507,0.0009728639,0.00753558],"genre_scores_gemma":[0.9701816,0.00003769165,0.0290551,0.0000161402,0.000004081158,0.000006748834,0.00001569658,0.00001296335,0.0006699323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001354494,"threshold_uncertainty_score":0.002693236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009824329575489742,"score_gpt":0.2068251924014559,"score_spread":0.1970008628259662,"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."}}