{"id":"W2048864718","doi":"10.1155/2013/767313","title":"Assessment of Different Sensor Configurations for Collaborative Driving in Urban Environments","year":2013,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada; Alberta Innovates; Alberta Innovates - Technology Futures; University of Calgary","keywords":"Differential GPS; Azimuth; Kalman filter; Global Positioning System; Range (aeronautics); Computer science; Geodesy; A priori and a posteriori; Extended Kalman filter; Reliability (semiconductor); Redundancy (engineering); Simulation; Remote sensing; Environmental science; Geography; Artificial intelligence; Mathematics; Engineering; Telecommunications; Geometry; 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":[0.001796653,0.0005855787,0.0005820104,0.0005091245,0.0004946219,0.0005808383,0.001037408,0.0008204338,0.0005168464],"category_scores_gemma":[0.006044777,0.000413618,0.000359471,0.0005137573,0.0004887223,0.001044889,0.0009607364,0.0002609693,0.000156836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004655098,"about_ca_system_score_gemma":0.0006562236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00354076,"about_ca_topic_score_gemma":0.006727547,"domain_scores_codex":[0.9991066,0.0002907758,0.00004173742,0.0001436096,0.0002732897,0.0001439259],"domain_scores_gemma":[0.9967076,0.001525135,0.0002682012,0.0005303478,0.000793857,0.000175002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00127112,0.0002643965,0.02359943,0.0001771969,0.00009095939,0.0002168806,0.0002643572,0.8867382,0.02361955,0.0009818318,0.0002188267,0.0625573],"study_design_scores_gemma":[0.00007732377,0.001607869,0.02671097,0.0000252515,0.0001097172,0.0002499863,0.0004998816,0.9400828,0.02886582,0.001017225,0.0007016474,0.00005155724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9224247,0.00008106923,0.07561477,0.00005718365,0.00001520307,0.00006211663,0.00006919995,0.000306464,0.001369372],"genre_scores_gemma":[0.9906171,0.00002153324,0.009200835,0.000003725068,0.000001296335,0.00001673606,0.00003374106,0.000006559875,0.00009846548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00354076,"threshold_uncertainty_score":0.009501696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281270690471323,"score_gpt":0.2494912506813397,"score_spread":0.2366785437766264,"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."}}