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Record W2380587679

Construction and accuracy evaluation of GPS/VRS positioning system with a multiple reference station network

2005· article· en· W2380587679 on OpenAlexaff
WU Yao-qiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsGlobal Positioning SystemComputer scienceReal Time KinematicReal-time computingRange (aeronautics)Position (finance)AzimuthTelecommunicationsRemote sensingGeographyEngineeringGNSS applications
DOInot available

Abstract

fetched live from OpenAlex

Multiple reference station networks have been established for high precision applications in many countries worldwide.However,real-time application is still a difficult task in practice.The virtual reference station (VRS) is an efficient method for the network users to perform RTK positioning.With the availability of GPRS or CDMA technology,an Internet-based VRS RTK positioning infrastructure has been developed and tested.This paper discusses the construction of GPS/VRS positioning system in Chengdu,China,which has been designed to offer centimeters magnitude service for potential public users located within the coverage.To make sure that the users within the reference station network can operate consistently at greater distances without degrading accuracy and to provide a reliable real-time kinematic positioning,field tests are presented to evaluate the performance of the system.The results demonstrate that Internet-based VRS RTK positioning can be achieved to better than 2.5 centimeters in horizontal position,and the height accuracy is in the range of 1 to 4.5 centimeters within the network.Distance-dependent errors,e.g.the ionospheric and troposhpheric delay errors,cannot been modeled effectively when rover receiver goes outside the reference-station-network coverage,especially for 120km or further from the network center,and the positioning accuracy degrade to decimeters.

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)0.000
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 score0.114
Threshold uncertainty score0.282

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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