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Record W2157659722 · doi:10.1109/drcn.2003.1275366

Structures of high-capacity reliable telecommunication networks

2004· article· en· W2157659722 on OpenAlexaff
M. Beshai, J. Fitchett, Alan Graves

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkEnhanced Data Rates for GSM EvolutionNode (physics)SimplicityReliability (semiconductor)Distributed computingCore networkCore (optical fiber)Set (abstract data type)Simple (philosophy)Agile software developmentTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Reliability is ascertained in an efficient agile network through structural simplicity, storage of route-set description at each edge node, rapid reporting of a failure to edge nodes that may be affected, and rapid rerouting of affected connections. Structural simplicity results in routes of a small number of hops each, thus enabling rapid rerouting. A proposed wide-coverage high-capacity network that scales to multiples of petabits per second and can encompass millions of edge nodes employs both fast-switching time-shared optical nodes and channel-switching optical core connectors. The core capacity is adaptively shared. Thus, failure of a proportion of core connectors may reduce network connectivity but would not result in a major service discontinuity. Several other simple structures can be devised to enable rapid reporting and rerouting, thus realizing economical highly-reliable networks.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 designTheoretical or conceptual
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

Citations2
Published2004
Admission routes1
Has abstractyes

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