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Record W7162013643 · doi:10.82308/5245

A scalable fiber optic local area network demonstrator /

2000· dissertation· en· W7162013643 on OpenAlexaboutno aff
Au, Albert Kar-Hing, 1972-

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsTestbedScalabilityInterconnectionSoftware deploymentWorkstationLocal area networkBandwidth (computing)Optical fiberData transmission

Abstract

fetched live from OpenAlex

The continuing advances in silicon integrated circuit technology in accordance with Moore's Law have fueled the constant performance improvement of computing systems. At the same time, the deployment of networks of workstations with high-speed processors has driven the need to increase bandwidth capacities in networked environments. Electrical interconnect technologies are plagued by substantial physical problems at high frequencies, that limit their data transmission capacities. With increasing clock rates, high-bandwidth computing and communication architectures become more difficult to implement using electrical interconnects, and therefore creates a strong motivation for exploring both free-space and fiber-based optical interconnect technologies. In this thesis, the development of a Fiber Optic Local Area Network (LAN) demonstrator is described. The demonstrator will be used as a testbed for research in high-speed networking technologies, lean protocols, and bandwidth-intensive network-oriented applications, which began at McGill University, Canada, and is being continued at McMaster University, Canada. (Abstract shortened by UMI.)

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.708
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations0
Published2000
Admission routes1
Has abstractyes

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