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Record W1874915917 · doi:10.1109/papcon.1997.595217

Design of high speed fiber optics backbones for pulp and paper mills

2002· article· en· W1874915917 on OpenAlexaboutno aff
E.J. Byres, E. Hanschke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFiber Distributed Data InterfaceComputer scienceLocal area networkIndustrial EthernetTroubleshootingComputer networkCarrier EthernetEthernetMetro EthernetOperating system

Abstract

fetched live from OpenAlex

In late 1995, the data communications networks at the Fletcher Challenge Canada's Crofton pulp and paper mill had reached capacity and was suffering from overload conditions. The large growth in process control information and personal computers over the past few years was straining the existing coax and fiber optic systems' ability to reliably transfer data. In addition, the increased network complexity made the troubleshooting of the networks extremely difficult and time-consuming. This paper discusses the design methods used to create a new fiber optic backbone that would provide the data carrying capacity and reliability needed for the pulp and paper mill's computers and control systems. High speed network technologies such as ATM (asynchronous transfer mode), FDDI (fiber distributed data interface) and fast Ethernet were compared for their suitability in industrial environments. The design also incorporated Ethernet switches so that the existing Ethernet networks could attach at minimal cost. These switches subdivided the single large network that originally existed into approximately 24 subnetworks. This division greatly increased the network's allowable traffic capacity and prevented problems in any one area from propagating throughout the mill, thus reducing network and process downtime.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.213
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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

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