{"id":"W4291713306","doi":"10.1139/dsa-2022-0010","title":"Non-redundant high-integrity position estimation robust to sensor bias jumps using MGLR","year":2022,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office National d'études et de Recherches Aérospatiales","keywords":"Position (finance); Computer science; GNSS applications; Consistency (knowledge bases); Inertial measurement unit; Drone; Maximum likelihood; Algorithm; Estimation; Global Positioning System; Statistics; Artificial intelligence; Engineering; Mathematics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001657855,0.0009529977,0.000840814,0.0007801463,0.0003464923,0.0008585926,0.001213252,0.0008740374,0.0007344637],"category_scores_gemma":[0.006724601,0.0003179268,0.0004945042,0.0005278207,0.0005572352,0.001580839,0.001568037,0.0007598497,0.0006788009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000321378,"about_ca_system_score_gemma":0.000492857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006964682,"about_ca_topic_score_gemma":0.0007409285,"domain_scores_codex":[0.9979712,0.0004919292,0.00007897131,0.0004000543,0.0009328582,0.0001249495],"domain_scores_gemma":[0.998022,0.0006454014,0.0004096274,0.0004606284,0.0003995625,0.00006284055],"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":[0.0009915708,0.0001540488,0.005544519,0.0003009449,0.0001929647,0.0003684566,0.000500121,0.1626153,0.2163655,0.006591571,0.001627465,0.6047477],"study_design_scores_gemma":[0.00006472814,0.0003861102,0.002954438,0.00002485505,0.00004899033,0.0004670998,0.0000595186,0.9105709,0.08051679,0.002389334,0.002454567,0.00006259807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07172009,0.0002477317,0.9241289,0.0001458658,0.00004173176,0.00004803525,0.0000430618,0.00205016,0.001574432],"genre_scores_gemma":[0.5925793,0.0001004256,0.4058566,0.0001381037,0.00003848242,0.00004639711,0.000148393,0.0002153025,0.0008769186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001657855,"threshold_uncertainty_score":0.008767724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04118803077070229,"score_gpt":0.2667014529288807,"score_spread":0.2255134221581784,"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."}}