{"id":"W2149469067","doi":"10.1109/iciea.2009.5138939","title":"Inter-vehicle range smoothing for NLOS condition in the persistence of GPS outages","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-line-of-sight propagation; Global Positioning System; Kalman filter; Computer science; Smoothing; Collision; Ranging; Range (aeronautics); Pseudorange; Real-time computing; Wireless; Telecommunications; Engineering; Computer security; Artificial intelligence; GNSS applications; Aerospace engineering; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001085034,0.0002268043,0.0004296481,0.000487732,0.0002730908,0.0002868333,0.0004381882,0.0003396464,0.0002176934],"category_scores_gemma":[0.007344874,0.0001861613,0.0002066552,0.0004829054,0.0004388288,0.0007045466,0.0004410654,0.0003808293,0.00006835419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002820123,"about_ca_system_score_gemma":0.0004327864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004261667,"about_ca_topic_score_gemma":0.004143154,"domain_scores_codex":[0.9996319,0.0001161751,0.00002408407,0.00006681839,0.0001128998,0.00004816573],"domain_scores_gemma":[0.996226,0.00237408,0.0006333608,0.0004149213,0.0002918435,0.00005988363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003547926,0.00004517154,0.03275304,0.0001039254,0.00008818505,0.0005164414,0.000520041,0.8394063,0.01239081,0.00379948,0.0003902445,0.1096315],"study_design_scores_gemma":[0.00001090256,0.00009298541,0.01250596,0.00001002886,0.00004234418,0.0002017254,0.0001108486,0.9811286,0.003179434,0.002258027,0.0004426505,0.00001646408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4583938,0.0004046061,0.5399438,0.0001719733,0.0000258737,0.0000125946,0.00002434123,0.0003295224,0.0006935021],"genre_scores_gemma":[0.9932303,0.00007633406,0.006528586,0.000008347162,0.000009272453,0.00000454334,0.00001251791,0.000008844942,0.0001212782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004261667,"threshold_uncertainty_score":0.008473754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972955172465248,"score_gpt":0.2409437471686879,"score_spread":0.2212141954440354,"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."}}