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Record W4221136582 · doi:10.5194/egusphere-egu22-1336

Modernizing Canadian Geodetic Survey’s precise GNSS orbit and clock system

2022· preprint· en· W4221136582 on OpenAlexaffabout
Mohammad Ali Goudarzi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGNSS applicationsGeodetic datumOrbit determinationOrbit (dynamics)GeodesyAmbiguity resolutionComputer scienceRemote sensingGlobal Positioning SystemGeographyTelecommunicationsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

To enable the new CSRS-PPP processing engine and online service to work in full multi-GNSS mode, and to provide high quality precise GNSS orbit and clock (POD) products to IGS and international geodetic community, Canadian Geodetic Survey (known as EMR) is modernizing his POD system. The new system is based on GipsyX and in-house software development and is replacing the current POD system. When becomes fully operational, the new POD system will produce multi-GNSS precise orbit and clock corrections with ambiguity resolution along with wide-lane and phase biases using zero-differenced, dual-frequency, ionosphere-free phase and code observations in RINEX 2 and 3 formats estimated in combined solution. The new system also benefits from advanced features such as removing observations and ground stations affected by ionospheric scintillations and earthquake, respectively, as well as real-time monitoring of estimated position time-series of ground stations, among others.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.036
GPT teacher head0.223
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2022
Admission routes2
Has abstractyes

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Same topicGNSS positioning and interferenceFrench-language works237,207