{"id":"W3046700762","doi":"10.5194/isprs-annals-v-1-2020-189-2020","title":"INVESTIGATION OF DIFFERENT LOW-COST LAND VEHICLE NAVIGATION SYSTEMS BASED ON CPD SENSORS AND VEHICLE INFORMATION","year":2020,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"GNSS applications; Accelerometer; Heading (navigation); Gyroscope; Inertial measurement unit; Dead reckoning; Inertial navigation system; GNSS augmentation; Computer science; Air navigation; Navigation system; Real-time computing; Sensor fusion; Global Positioning System; Engineering; Artificial intelligence; Telecommunications; Inertial frame of reference; Aerospace engineering","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":[0.0002012458,0.0002698718,0.0003351127,0.0004988629,0.0001700101,0.0003407493,0.0005131013,0.000312713,0.00112568],"category_scores_gemma":[0.0006079252,0.0001314759,0.0001684996,0.0003043999,0.0001688104,0.0004093467,0.0002390902,0.0001433178,0.0002153903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651048,"about_ca_system_score_gemma":0.000198461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004493,"about_ca_topic_score_gemma":0.00256004,"domain_scores_codex":[0.9995854,0.00007317949,0.00001476668,0.00006686928,0.0002275947,0.00003213096],"domain_scores_gemma":[0.999663,0.0000749899,0.00005163254,0.00004141652,0.0001500036,0.00001896147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001401104,0.0002743104,0.03951322,0.0006402558,0.0001738738,0.0006898958,0.0003060696,0.02958164,0.7581856,0.0009285014,0.001153055,0.1671525],"study_design_scores_gemma":[0.0001290438,0.003557154,0.10783,0.00004601434,0.0002093894,0.0007801053,0.0003918069,0.3506549,0.5277358,0.0003149245,0.008294526,0.00005631797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687375,0.0003204728,0.02831325,0.00004722158,0.00004322787,0.00006981467,0.0001163574,0.0003094096,0.002042739],"genre_scores_gemma":[0.9934949,0.00006584365,0.005652557,0.0000143001,0.000003265335,0.00001405055,0.00008287069,0.00000623223,0.0006659245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002004493,"threshold_uncertainty_score":0.003985703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03117898234220306,"score_gpt":0.242996687631223,"score_spread":0.21181770528902,"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."}}