{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003138534,0.0001064721,0.0001437703,0.0001582642,0.0001173274,0.0001280136,0.00008285405,0.00005366642,4.759809e-7],"category_scores_gemma":[0.00007778792,0.00007957862,0.00003579987,0.0003544388,0.0001534455,0.0004146465,0.00002747538,0.00008674024,8.953433e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008550429,"about_ca_system_score_gemma":0.00001082453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00408878,"about_ca_topic_score_gemma":0.00009171919,"domain_scores_codex":[0.9990492,0.0000439932,0.0003861653,0.00006969575,0.0003342957,0.0001166576],"domain_scores_gemma":[0.9994776,0.00004840113,0.000205248,0.00009664564,0.0001011101,0.00007104854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004148671,0.000004613175,0.001316407,0.0005348827,0.00001698854,1.00572e-7,0.001855377,0.08886269,0.002471456,0.00002929207,0.0008270969,0.9040396],"study_design_scores_gemma":[0.0001888914,0.00008062535,0.005457966,0.0001766972,0.000007210335,7.086503e-7,0.0002883986,0.9530105,0.04037649,0.00002089959,0.0003133618,0.00007823359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6891175,0.00001267096,0.3088662,0.000851973,0.0001743051,0.0003753078,0.00002115556,0.0002288523,0.000351991],"genre_scores_gemma":[0.9993395,0.00004497203,0.0001319437,0.0004478706,0.00001547543,4.595554e-7,0.00001588407,0.000003327058,6.281719e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9039614,"threshold_uncertainty_score":0.618104,"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."}}