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Record W4304112907 · doi:10.1088/1681-7575/ac98cb

Achieving traceability to UTC through GNSS measurements

2022· article· en· W4304112907 on OpenAlexaff
Pascale Defraigne, Joseph Achkar, Michael J. Coleman, Marina Gertsvolf, Ryuichi Ichikawa, Judah Levine, Pierre Uhrich, Peter Whibberley, Maurice G.A.J. Wouters, A. Bauch

Bibliographic record

VenueMetrologia · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsGNSS applicationsTraceabilityMetrologyComputer scienceGlobal Positioning SystemTelecommunicationsFrequency offsetSynchronization (alternating current)Offset (computer science)StandardizationUTC offsetSatellite systemSystems engineeringRemote sensingReal-time computingEngineeringOrthogonal frequency-division multiplexingGeographySoftware engineering

Abstract

fetched live from OpenAlex

Abstract Coordinated universal time (UTC) is the international reference for time and frequency measurement, and the basis of civil timekeeping world-wide. The reception of signals from global navigation satellite systems (GNSS) as a source of time and frequency (synchronization and syntonization) has found widespread use in virtually all user sectors, including electrical power supply, telecommunications, and financial institutions. This paper summarizes the concept of metrological traceability and the practices employed in the time and frequency metrology community for achieving it. Practical steps are proposed to ensure that traceability to UTC from GNSS signal reception is available to a wide community of users, addressing different levels of required uncertainty in time and frequency offset from UTC. We suggest some practical measures that can be followed by users, and improvements to the services provided by National Metrology Institutes (NMIs).

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.308
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

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 designBench or experimental
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

Citations19
Published2022
Admission routes1
Has abstractyes

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