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Record W2107297335 · doi:10.1109/aina.2007.83

Integrated TDM in Single-Hop Slotted All-Optical OPS Networks

2007· article· en· W2107297335 on OpenAlexaff
Akbar Ghaffarpour Rahbar, Oliver Yang

Bibliographic record

VenueProceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkReservationEnhanced Data Rates for GSM EvolutionScheme (mathematics)Network packetPacket lossEdge deviceArchitectureDistributed computingShared resourcePacket switchingOptical burst switchingHop (telecommunications)Wavelength-division multiplexingTelecommunicationsOptical performance monitoringCloud computing

Abstract

fetched live from OpenAlex

We study and evaluate the performance of three potential candidates of resource-sharing algorithms in agile single-hop slotted buffer-less all-optical packet switched metro networks. Both reservation-based (centralized) and collision-based (distributed) schemes are considered. In the centralized scheme, all decisions are made at the core switch and loss can be avoided through reservations. On the other hand, decisions are made at the edge switches in the distributed scheme, and we need to retransmit the dropped traffic. We also implement an integrated architecture that combines the good attributes of both schemes and characterize this architecture by various measures such as delay and loss probabilities at the edge switches.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations1
Published2007
Admission routes1
Has abstractyes

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