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Record W2965239623 · doi:10.1002/navi.323

Analysis of GNSS correction data standards for the automotive market

2019· article· en· W2965239623 on OpenAlex
Sudha Vana, John Aggrey, Sunil Bisnath, Rodrigo Leandro, Landon Urquhart, PAOLA YEPEZ GONZALEZ

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2019
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsYork University
Fundersnot available
KeywordsGNSS applicationsAutomotive industryFlexibility (engineering)Computer scienceContext (archaeology)TelecommunicationsBandwidth (computing)EngineeringGlobal Positioning SystemAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, a new standard that has been developed by Sapcorda Services to target the specific requirements of high-precision GNSS technology in the automotive and mass market industry is assessed within the context of existing data standards. This new standard was created as a joint effort of several organizations and has similarities with the Radio Technical Commission for Maritime Services (RTCM) v3 standard and compact state-space representation messages (CSSR). However, it has different message design rules that specifically target automotive and mass market sectors. Results indicate significant reduction in bandwidth usage particularly for the atmosphere component, as the new format consumes 15% less bandwidth compared with the all-purpose existing formats, increased end-to-end positioning performance, and integrity, as well as flexibility for future growth of GNSS correction services.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.280
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it