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Record W2138039764 · doi:10.1109/mnet.2008.4694173

Trends in Optical Switching Techniques: A Short Survey

2008· article· en· W2138039764 on OpenAlexaff
Martin Maier, Martin Reisslein

Bibliographic record

VenueIEEE Network · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBottleneckComputer science10G-PONOptical burst switchingTelecommunicationsOptical switchMultiwavelength optical networkingBandwidth (computing)Optical performance monitoringPower consumptionPassive optical networkOptical cross-connectComputer networkOptical fiberElectronic engineeringWavelength-division multiplexingPower (physics)Fiber optic splitterEngineeringEmbedded systemMaterials science

Abstract

fetched live from OpenAlex

We are currently witnessing a strong worldwide push toward bringing fiber closer to individual homes and businesses. The emerging FTTX access networks will move the bandwidth bottleneck from the first/last mile toward metropolitan and wide area networks, creating a need for efficient optical-switching mechanisms. In this article, we review the current trends in optical switching that help to improve the bandwidth efficiency, as well as to decrease the cost and power consumption of next-generation optical networks. Our review provides an overview of the optical switching domain and facilitates the understanding of newly emerging switching techniques and their interpretation as derivatives of the presented main optical switching trends.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2008
Admission routes1
Has abstractyes

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