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Record W2316600281 · doi:10.1515/jag-2014-0009

An Innovative Dual Frequency PPP Model for Combined GPS/Galileo Observations

2014· article· en· W2316600281 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Geodesy · 2014
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsToronto Metropolitan University
FundersU.S. Naval ObservatoryNatural Sciences and Engineering Research Council of Canada
KeywordsGalileo (satellite navigation)Global Positioning SystemPrecise Point PositioningSatelliteComputer scienceGeodesyGNSS applicationsOffset (computer science)UTC offsetRemote sensingGeographyAerospace engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract This paper develops a new dual-frequency precise point positioning model, which combines GPS and Galileo observables. The addition of Galileo satellite system offers more visible satellites to the user, which is expected to enhance the satellite geometry and the overall PPP solution in comparison with GPS-only PPP solution. However, combining GPS and Galileo observables introduces additional biases, which require rigorous modelling, including the GPS to Galileo time offset, and Galileo satellite hardware delay. In this research, a GPS/Galileo ionosphere-free linear combination PPP model is developed. The additional biases of the GPS/Galileo combination are lumped and accounted for through the introduction of a new unknown parameter, inter-systems bias, in the PPP mathematical model. It is shown that a subdecimeter positioning accuracy level and 25% reduction in the solution convergence time can be achieved with the developed GPS/Galileo PPP model.

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)0.000
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 score0.321
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.243
Teacher spread0.217 · 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