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Record W2519748055 · doi:10.1109/fcs.2016.7546777

Long-term GPS carrier-phase time transfer noise: A study based on seven GPS receivers at NIST

2016· article· en· W2519748055 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
FundersNatural Resources CanadaNational Institute of Standards and Technology
KeywordsGlobal Positioning SystemTime transferNISTPhase noiseTerm (time)Phase (matter)GPS disciplined oscillatorNoise (video)Computer scienceGPS signalsCalibrationGps receiverAssisted GPSGeodesyRemote sensingElectronic engineeringTelecommunicationsPhysicsGeographyStatisticsEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper reports a preliminary study of the long-term GPS carrier-phase time transfer noise, based on seven NIST GPS receivers. We find that the difference in carrier-phase time-transfer result using any two GPS receivers over 100 days can change by ~ 0.5 ns (peak-to-peak) in most cases and by ~ 1.3 ns (peak-to-peak) in some extreme cases. This suggests that a more frequent GPS calibration is needed to achieve sub-nanosecond GPS timing accuracy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.973

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)0.0280.001

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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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