{"id":"W2898966489","doi":"10.1007/1345_2018_45","title":"Assessment of GNSS and Map Integration for Lane-Level Applications in the Scope of Intelligent Transportation Location Based Services (ITLBS)","year":2018,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"GNSS applications; Computer science; Sensor fusion; Kalman filter; Map matching; Context (archaeology); Global Positioning System; Intelligent transportation system; Identification (biology); Real-time computing; Computer vision; Artificial intelligence; Geography; Engineering; Transport 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":[0.0004704932,0.0001653408,0.0002772904,0.0002582236,0.00002810595,0.00000981307,0.0002670262,0.000318224,0.00004053941],"category_scores_gemma":[0.00001219923,0.0001540971,0.00008428463,0.00006245304,0.00005654512,0.00009270968,0.000008030998,0.0001610104,0.000001706583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585028,"about_ca_system_score_gemma":0.00006159196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004444243,"about_ca_topic_score_gemma":0.0006041588,"domain_scores_codex":[0.9987059,0.0000170522,0.0007124725,0.0001651486,0.0003132722,0.00008613985],"domain_scores_gemma":[0.998356,0.0002686749,0.000661346,0.0001583947,0.0005425107,0.00001305124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000107041,0.0003192371,0.01864623,0.002326191,0.001024933,5.14342e-7,0.002917486,0.02119054,0.00745717,0.9296191,0.0005030766,0.0158885],"study_design_scores_gemma":[0.004924859,0.001052378,0.4588543,0.004869008,0.00126147,0.000003264775,0.001784549,0.2593378,0.06475662,0.1246742,0.07655365,0.001927865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08851522,0.002106281,0.8030183,0.003124346,0.001352649,0.00764114,0.007303309,0.0003640431,0.08657476],"genre_scores_gemma":[0.9936827,0.000203214,0.002656139,0.00002872833,0.00004807722,0.0001276401,0.001745517,0.00002485256,0.001483111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9051675,"threshold_uncertainty_score":0.6283895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031881201666601,"score_gpt":0.2482527520804263,"score_spread":0.2379339400637603,"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."}}