MétaCan
Menu
Back to cohort
Record W1514797997 · doi:10.1109/icc.1991.162435

Photonic bus metropolitan area networks

2002· article· en· W1514797997 on OpenAlexaff
T.D. Todd, A.M. Bignell, S. Sivakumaran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceComputer networkProtocol (science)Block (permutation group theory)PhotonicsMetropolitan area networkQueueing theoryNetwork topologyStar networkExploitLocal area networkTopology (electrical circuits)EngineeringElectrical engineeringRing network

Abstract

fetched live from OpenAlex

Protocol design and receiver allocation for photonic bus networks (PBNets) are considered. The basic building block for PBNets consists of a linear multihop network interconnected in a bidirectional bus virtual-topology using point-to-point optical channels. Although the system may be implemented using any optical broadcast device, PBNets are designed to exploit transmissive optical star technology. A new media access protocol referred to as REQPASS (request passing protocol) is proposed for the system. An analytic model which accurately predicts individual station queuing delay is introduced. New algorithms are also proposed for receiver allocation in PBNets.>

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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
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

Citations8
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Optical Network TechnologiesFrench-language works237,207