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Record W2183507464

Network Real-Time Kinematic Performance Analysis Using Rtcm 3.0 and the Southern Alberta Network

2007· article· en· W2183507464 on OpenAlexvenueaboutno aff
Kyle O’Keefe, Gérard Lachapelle

Bibliographic record

VenueGEOMATICA · 2007
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsAmbiguity resolutionComputer scienceReal Time KinematicReal-time computingKinematicsDifferential GPSInterpolation (computer graphics)GNSS applicationsGlobal Positioning SystemRemote sensingGeographyComputer networkTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The RTCM 3.0 data transmission format is introduced and described as it applies to multiple reference station real-time kinematic differential GPS positioning, or network RTK. The new format provides a more modern and flexible message structure that accommodates network RTK data transmission, while requiring up to 80% less bandwidth for the transmission of RTK correction messages. Based on this reduced bandwidth, we investigated moving the correction interpolation step of the network RTK procedure from the network to the rover user. The RTCM 3.0 format is implemented and tested in both network and user software. Three interpolation methods are implemented and compared with single baseline processing using real data collected using reference stations from the Southern Alberta Network. Under moderate ionospheric conditions, the network RTK solution outperforms the single baseline approach in both the observation and position domains. The three interpolation methods are found to be comparable. Under severe ionospheric conditions, network ambiguity resolution becomes difficult, making the network corrections unreliable. It is recommended that a network ambiguity resolution status flag be added to the RTCM 3.0 network correction message format to alert users when network corrections are unreliable.

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.

How this classification was reachedexpand

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 score0.048
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes2
Has abstractyes

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