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Record W2065435001 · doi:10.1109/mvt.2012.2203691

Data Transport Networks: Case Studies in a Public Transit Environment

2012· article· en· W2065435001 on OpenAlexfundno aff
M FITZMAURICE

Bibliographic record

VenueIEEE Vehicular Technology Magazine · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsnot available
FundersNational Taipei University of TechnologyUniversity of Ottawa
KeywordsMetro EthernetSynchronous optical networkingComputer networkEthernetTelecommunicationsPublic transportCircuit switchingEthernet over SDHConnection-oriented EthernetSwitched communication networkComputer scienceContext (archaeology)EngineeringCarrier EthernetEthernet flow controlTransport engineering

Abstract

fetched live from OpenAlex

Ethernet has become the de facto standard for providing data transport not only throughout the Internet but also for numerous private and industrial networks. Before Ethernet's dominance, circuit-switched networks (or time-division multiplexed networks such as synchronous optical network (SONET) are popular choices for applications that require flexible and reliable data transport. Over the past ten years, packet-switched networks in general (and Ethernet in particular) usurped the role formerly played by these circuit-switched networks. But is this a good development given that not all applications that require data transport are created equally? This article explores the current trend of using packet-switched as opposed to circuit-switched networks in the context of providing data transport for rail-based public transit agencies and, after examining five case studies, draws conclusions that are relevant for public transit agencies.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.270
Teacher spread0.221 · 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 designObservational
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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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