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Record W2012922054 · doi:10.1002/nem.696

A network management tool for resource‐partition based layer 1 virtual private networks

2008· article· en· W2012922054 on OpenAlex
Jing Wu, Michel Savoie, Scott Campbell, Hanxi Zhang

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Network Management · 2008
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsCommunications Research Centre Canada
FundersDalhousie UniversityCanarie
KeywordsComputer sciencePrivate networkPartition (number theory)Computer networkDatabase transactionApplication layerNetwork elementDistributed computingDatabaseOperating system

Abstract

fetched live from OpenAlex

Abstract A Layer 1 Virtual Private Network (L1‐VPN) has two models for service management: the resource‐partition based model and the domain‐service based model. In this paper, we present a network management tool for resource‐partition based L1‐VPNs. A Transaction Language One (TL1) proxy is designed to achieve resource partitioning at the network element level. Building on top of a TL1 proxy, we implemented a User‐Controlled LightPath (UCLP) system to support physical network brokers to assign and allocate virtually dedicated resources to customers, and to enable customers to directly manage their resources. With such a capability, customers are able to create wide area networks based on their traffic pattern, and to adjust their traffic pattern based on available resources. Copyright © 2008 Crown in the right of Canada. Published by 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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