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Record W2031032271 · doi:10.5589/q04-001

Global Navigation Satellite Systems — Capabilities and Opportunities

2004· article· en· W2031032271 on OpenAlexaffvenue
Gérard Lachapelle, M. Elizabeth Cannon

Bibliographic record

VenueCanadian aeronautics and space journal · 2004
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGNSS applicationsGlobal Positioning SystemGNSS augmentationGalileo (satellite navigation)Satellite systemInertial navigation systemSatellite navigationComputer scienceSatelliteTelecommunicationsEuropean unionReal Time KinematicReal-time computingSystems engineeringRemote sensingEngineeringGeographyInertial frame of referenceAerospace engineeringBusiness

Abstract

fetched live from OpenAlex

The capabilities of the current global navigation satellite system (GNSS), namely, the U.S. Global Positioning System (GPS), are first reviewed and the error sources affecting system performance are briefly described. Selected advances made recently to improve its accuracy, reliability, and availability are discussed and illustrated with examples. These include real-time kinematic (RTK) positioning, integration with self-contained inertial navigation systems (INS), attitude determination, and "indoor GPS". The latter, which deals with signal availability using weak signals, has resulted in intense research and development activities. It has also launched a rapid expansion of location-based services (LBS), a market expected to grow to over US $25B worldwide annually by the end of the decade. The characteristics and advantages of the second generation GPS, namely GPS II, that will become available throughout the decade ahead are summarized. Finally, the major characteristics and impact of Galileo, the European Union's GNSS, also to be deployed this decade, are discussed. The major conclusion is that GNSS multiplicity and performance improvements will have a major impact on the user community.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.897

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 head0.016
GPT teacher head0.200
Teacher spread0.183 · 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 designTheoretical or conceptual
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
Published2004
Admission routes2
Has abstractyes

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