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Record W2304524573

GPS Ambiguity Resolution and Validation: Methodologies, Trends and Issues

2000· article· en· W2304524573 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAmbiguity resolutionAmbiguityGlobal Positioning SystemComputer scienceResolution (logic)Range (aeronautics)Remote sensingData scienceGeographyArtificial intelligenceTelecommunicationsEngineeringGNSS applications
DOInot available

Abstract

fetched live from OpenAlex

Resolving the GPS carrier-phase ambiguities has been a continuing challenge for sub -centimeter-level highprecision GPS positioning. Once the integer ambiguities are fixed correctly, the carrie r-phase observations are conceptually turned into sub-centimeter-level high-precision range measurements making it possible to attain high-precision positioning solutions. Consequently, this topic has been a rich source of GPS -research over the last decade. A brief review of the previous work on ambiguity resolution and validation which has been carried out by many research groups from all over the world is presented in this paper. For a general understanding of these contributions, we have classified the ambiguity resolution techniques in terms of their characteristics. Current research trends and issues in ambiguity resolution and validation are described and a bibliography of representative papers is provided.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.592

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)0.0010.000

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 head0.058
GPT teacher head0.308
Teacher spread0.250 · 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

Quick stats

Citations106
Published2000
Admission routes1
Has abstractyes

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