{"id":"W2987707710","doi":"10.3390/s19224896","title":"Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Trusted Positioning (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"GNSS applications; Precise Point Positioning; Real-time computing; Satellite system; Inertial navigation system; Computer science; Global Positioning System; Navigation system; Metre; Simulation; Telecommunications; Inertial frame of reference","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":[0.0002410525,0.0003790566,0.0003086025,0.0003921137,0.0002371107,0.000427076,0.0006672512,0.0004018758,0.002065709],"category_scores_gemma":[0.0004812615,0.0001722126,0.0001751121,0.0003819812,0.000201905,0.0006630528,0.0005520102,0.0003891542,0.001372563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835953,"about_ca_system_score_gemma":0.0005176922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002497539,"about_ca_topic_score_gemma":0.002410437,"domain_scores_codex":[0.999626,0.00003873112,0.00001196582,0.00005521243,0.0002338473,0.00003429941],"domain_scores_gemma":[0.9997491,0.00002060644,0.00002756516,0.00004589703,0.0001445815,0.00001229526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003475978,0.0001364571,0.0119559,0.0003221368,0.00005939857,0.0006703821,0.0002841687,0.03234793,0.2908502,0.003934551,0.00764576,0.6514456],"study_design_scores_gemma":[0.0001128345,0.001277759,0.03201075,0.00007632222,0.0001651104,0.001581946,0.0003294233,0.5524888,0.3091373,0.003233925,0.09945679,0.0001290215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1685051,0.0005409247,0.8124445,0.000205662,0.0002218649,0.0001574655,0.0003519988,0.007690646,0.009881843],"genre_scores_gemma":[0.8377489,0.0002490502,0.1551464,0.00008081113,0.00005067865,0.00008120606,0.0007790175,0.0001487545,0.005715244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002497539,"threshold_uncertainty_score":0.006910503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007113062002300298,"score_gpt":0.2210510081336407,"score_spread":0.2139379461313404,"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."}}