{"id":"W4414184914","doi":"10.3390/engproc2025088075","title":"Enhancing GNSS Robustness in Automotive Applications with Supercorrelation: Experimental Results in Urban Scenarios","year":2025,"lang":"en","type":"article","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trusted Positioning (Canada)","funders":"","keywords":"Multipath propagation; GNSS applications; Robustness (evolution); Multipath interference; Satellite system; Interference (communication); Precise Point Positioning; Global Positioning System; Automotive industry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000656266,0.00009579683,0.0001007142,0.0001703329,0.0000338519,0.00002767322,0.00008239469,0.0000531082,0.00001956361],"category_scores_gemma":[0.000008227368,0.00009193052,0.00001197354,0.0004004323,0.00002062439,0.0001516503,0.00001565294,0.0001601082,0.00001206229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001810175,"about_ca_system_score_gemma":0.00002532093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128435,"about_ca_topic_score_gemma":0.0006018237,"domain_scores_codex":[0.9994022,0.00001439883,0.0002121799,0.0001682306,0.00005792528,0.000145046],"domain_scores_gemma":[0.9997638,0.00004036538,0.000009172232,0.0001370511,0.00002803157,0.00002160589],"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":[0.00006528742,0.0002194045,0.008269596,0.00005550015,0.00002275668,0.000006216964,0.005884968,0.967591,0.01061559,0.005913241,0.0007966199,0.0005597434],"study_design_scores_gemma":[0.002642394,0.0001164816,0.03913321,0.001249039,0.00001059841,0.000008837459,0.005955426,0.6898344,0.2600433,0.00006502945,0.0004112336,0.0005300883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7669264,0.0001844959,0.1395027,0.0001299929,0.000113934,0.000390203,0.000004400233,0.0002995223,0.09244835],"genre_scores_gemma":[0.9974212,0.000002476039,0.00163859,0.00002203241,0.00001306469,0.0001679402,0.00001693459,0.000008475708,0.0007092595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2777567,"threshold_uncertainty_score":0.3748818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006105069607938391,"score_gpt":0.2236524733332005,"score_spread":0.2175474037252621,"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."}}