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A New Method for Carrier-Phase-Based Precise Point Positioning

2002· article· en· W2134020105 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

VenueNAVIGATION Journal of the Institute of Navigation · 2002
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceConvergence (economics)Point (geometry)Integer (computer science)Phase (matter)Precise Point PositioningAmbiguityStability (learning theory)Filter (signal processing)AlgorithmReal-time computingTelecommunicationsComputer visionGlobal Positioning SystemMathematicsMachine learning

Abstract

fetched live from OpenAlex

ABSTRACT: This paper describes a carrier-phase–based precise point positioning (PPP) method based a new observation model. Unlike the traditional PPP processing method, the new method allows for exploitation of the integer characteristics of carrier-phase ambiguities. The new method also opens the door for the development of new algorithms for ambiguity resolution to support real-time PPP applications in the future. Numerical results indicate that the new method is superior to the traditional PPP processing method in terms of positional accuracy, convergence speed, and filter stability.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.429

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 communication0.0000.001
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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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