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Record W1507914537

Impact of tunable wavelength converter performance on all-optical wavelength-routing switches for data centers

2012· article· en· W1507914537 on OpenAlexaff
Houman Rastegarfar, Alberto Leon‐Garcia, Sophie LaRochelle, Leslie A. Rusch

Bibliographic record

VenueInternational Conference on Photonics in Switching · 2012
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversité LavalUniversity of Toronto
Fundersnot available
KeywordsWavelengthOptical switchOptical performance monitoringRouting (electronic design automation)Wavelength-division multiplexingOptical cross-connectConvertersComputer scienceOptoelectronicsOptical burst switchingMaterials scienceElectronic engineeringComputer networkTelecommunicationsElectrical engineeringOptical fiberEngineeringVoltage
DOInot available

Abstract

fetched live from OpenAlex

All-optical wavelength converters are central to next-generation exascale communication networks that span millions of processing cores. We examine the loss performance of a wavelength-routing optical packet switch by focussing on tunable wavelength converter parameters. Our cross-layer analysis reveals the stringent wavelength conversion requirements in terms of optical signal-to-noise ratio and conversion efficiency.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.329
Teacher spread0.247 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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