MétaCan
Menu
Back to cohort
Record W1973148104 · doi:10.1002/nem.583

Clock synchronization using a linear process model

2005· article· en· W1973148104 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

VenueInternational Journal of Network Management · 2005
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsClock driftTransmitterComputer scienceClock synchronizationTimestampSynchronization (alternating current)Digital clock managerSelf-clocking signalClock domain crossingClock skewProcess (computing)Real-time computingAlgorithmSynchronous circuitClock signalTelecommunicationsJitterChannel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract In this paper, we present a clock synchronization scheme based on a simple linear process model which describes the behaviors of clocks at a transmitter and a receiver. In the clock synchronization scheme, a transmitter sends explicit time indications or timestamps to a receiver, which uses them to synchronize its local clock to that of the transmitter. Here, it is assumed that there is no common network clock available to the transmitter and the receiver and, instead, the receiver relies on locking its clock to the arrival of the timestamps sent by the transmitter. The clock synchronization algorithm used by the receiver is based on a weighted least‐squares criterion. Using this algorithm, the receiver observes and processes several consecutive clock samples (timestamps) to generate accurate timing signals. This algorithm is very efficient computationally, and requires the storage of only a small number of clock samples in order to generate accurate timing signals. Copyright © 2006 John Wiley & Sons, Ltd.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.599
Threshold uncertainty score0.539

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 science0.0020.000
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 head0.015
GPT teacher head0.287
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