{"id":"W2102306504","doi":"","title":"PEDESTRIAN AND VEHICULAR NAVIGATION UNDER SIGNAL MASKING USING INTEGRATED HSGPS AND SELF CONTAINED SENSOR TECHNOLOGIES","year":2003,"lang":"en","type":"article","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; SIGNAL (programming language); Accelerometer; GPS signals; Interference (communication); Reliability (semiconductor); Real-time computing; Masking (illustration); Assisted GPS; Engineering; Telecommunications","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":[0.00006641171,0.0001053583,0.00009641963,0.0000580749,0.0000739453,0.00007922698,0.00002575293,0.0000914203,0.00000944105],"category_scores_gemma":[0.00001657283,0.00009383111,0.00001096412,0.00009544089,0.00003660971,0.000106171,0.000009258723,0.0001275409,0.000001633299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004173575,"about_ca_system_score_gemma":0.000008964192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003496839,"about_ca_topic_score_gemma":0.000003946422,"domain_scores_codex":[0.9995756,0.00001959667,0.000108784,0.0001189995,0.00004841741,0.00012861],"domain_scores_gemma":[0.9998373,0.00002740173,0.00001484105,0.00006374054,0.00003276637,0.00002397688],"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":[0.00003161173,0.0001191854,0.01777403,0.0004902943,0.0004504639,0.00006456134,0.002363745,0.1004143,0.8041324,0.06525712,0.000174668,0.008727665],"study_design_scores_gemma":[0.0007896672,0.0001191778,0.00127684,0.0003357547,0.00006335845,0.0003047702,0.006469598,0.7626794,0.2240178,0.003073263,0.0003885606,0.0004817736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9136314,0.0002410914,0.0841374,0.00002793605,0.00003690988,0.00007021223,0.000001200084,0.0006205151,0.001233362],"genre_scores_gemma":[0.9918722,0.00002616289,0.008043223,0.000009826226,0.000004954631,0.000002777507,0.000004108808,0.00001168854,0.000025074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6622651,"threshold_uncertainty_score":0.3826322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121865648323315,"score_gpt":0.2102603119495083,"score_spread":0.1980737471171768,"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."}}