{"id":"W2940670565","doi":"10.22215/etd/2017-12119","title":"Instrumentation and application of unmanned ground vehicles for magnetic surveying","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Resources Canada","keywords":"Magnetometer; Envelope (radar); Aerospace engineering; Noise (video); Unmanned ground vehicle; Instrumentation (computer programming); Engineering; Remote sensing; Planetary exploration; Computer science; Aeronautics; Artificial intelligence; Magnetic field; Geography; 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":[0.00008975244,0.0001253343,0.00016721,0.00008993617,0.00007603758,0.00005087366,0.00006447837,0.0001436889,0.000005646546],"category_scores_gemma":[0.0000186748,0.0001359941,0.00003037042,0.00003446508,0.00001381749,0.00008096753,0.0000023001,0.00004417744,0.00000120639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002149562,"about_ca_system_score_gemma":0.00001049228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001296104,"about_ca_topic_score_gemma":0.0004707635,"domain_scores_codex":[0.9994524,0.000008580741,0.0002273158,0.0001365203,0.00008683162,0.0000884231],"domain_scores_gemma":[0.9995949,0.00004217378,0.0001032651,0.000149671,0.0000857518,0.00002424617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001378365,0.00005331167,0.002645356,0.00559782,0.0001303042,3.938324e-7,0.00107783,0.05858116,0.0751861,0.01120664,0.0003340236,0.8450492],"study_design_scores_gemma":[0.0009378538,0.0001369,0.06780776,0.0001619318,0.0001391719,7.464037e-7,0.0006272867,0.8982406,0.02882786,0.002095247,0.0005714488,0.0004532382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8685925,0.0005919233,0.1243345,0.00001352021,0.000470641,0.001042353,0.00003950191,0.0001183203,0.004796717],"genre_scores_gemma":[0.9949856,0.0001921311,0.001487748,0.000003504424,0.00003562271,0.00005434039,0.002364933,0.00004012395,0.0008359778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.844596,"threshold_uncertainty_score":0.5545676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110715299565652,"score_gpt":0.2494010399162906,"score_spread":0.2382938869206341,"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."}}