{"id":"W2298560165","doi":"10.4271/2004-01-0748","title":"A Portable Vehicular Navigation System Using High Sensitivity GPS Augmented with Inertial Sensors and Map-matching","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"GNSS positioning and interference","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"AUTO21 Network of Centres of Excellence","keywords":"Global Positioning System; Inertial measurement unit; Computer science; Map matching; Multipath propagation; Inertial navigation system; Sensitivity (control systems); GPS/INS; GPS signals; Real-time computing; Assisted GPS; Sensor fusion; Noise (video); Position (finance); Remote sensing; Computer vision; Inertial frame of reference; Geography; Engineering; Electronic engineering; Telecommunications; Channel (broadcasting)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003867643,0.0006209081,0.000694254,0.0001463124,0.0003882585,0.0001599743,0.0002137913,0.0004974438,0.0000176712],"category_scores_gemma":[0.00006027213,0.0005396502,0.0001415911,0.0004173402,0.0004098237,0.0005454748,0.0001269608,0.0009776645,0.00002462745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005737738,"about_ca_system_score_gemma":0.00005430713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004488478,"about_ca_topic_score_gemma":0.008836407,"domain_scores_codex":[0.9972191,0.0001033284,0.000663297,0.0007429771,0.0005883051,0.0006829792],"domain_scores_gemma":[0.998751,0.0001198042,0.0001298137,0.0006286419,0.00009988758,0.0002708378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000134432,0.00008717362,0.00006069104,0.000205367,0.00006622497,0.0002127916,0.00005718515,0.02800108,0.9649137,0.006058097,0.00004427888,0.0001589446],"study_design_scores_gemma":[0.002354226,0.001552199,0.9744182,0.005649054,0.0003450082,0.002796429,0.0006181177,0.00006596518,0.008659476,0.001015925,0.0006577607,0.00186768],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930883,0.0001260527,0.00006741845,0.000359981,0.0001874442,0.0005133812,0.00004101705,0.003040446,0.00257593],"genre_scores_gemma":[0.9936873,0.000022832,0.005773997,0.0001263514,0.0001061619,0.00006740443,0.00006912158,0.0001199399,0.00002692205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9743575,"threshold_uncertainty_score":0.9997055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006728924408582215,"score_gpt":0.2048857800833831,"score_spread":0.1981568556748009,"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."}}