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Record W2969472313 · doi:10.1109/mcom.2019.8808153

High-Speed Copper and Coaxial Broadband

2019· article· en· W2969472313 on OpenAlexaff
Jochen Maes, Rainer Strobel, Anas Al Rawi, Mahdia Ben-Ghorbel

Bibliographic record

VenueIEEE Communications Magazine · 2019
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsExfo Electro-Optical Engineering (Canada)
Fundersnot available
KeywordsBroadbandLast mile (transportation)Coaxial cableCoaxialGigabitComputer scienceTelecommunicationsBroadband networksSoftware deploymentHybrid fibre-coaxialTransmission (telecommunications)Access networkTwisted pairInternet accessComputer networkThe InternetPassive optical networkWavelength-division multiplexingMaterials sciencePhysicsOperating systemMileOptoelectronics

Abstract

fetched live from OpenAlex

The articles in this special section addresses copper technologies and deployment practices beyond those currently available. Legacy copper infrastructure originally designed to provide voice services (twisted pair network) or television services (coaxial network) has performed vastly beyond specification and continues to deliver increased broadband speeds. Today’s mature technologies like vectored VDSL2 and G.fast for twisted pair and DOCSIS 3.1 for coaxial networks, are able to surpass present-day demand for broadband speeds in excess of several hundreds Mb/s per end user. The ability to off er gigabit speeds has been fueled by advances in digital signal processing and by a steady migration toward fiber rich access networks where only the last “mile” into the homes remains copper based (FTTx). We often get the question why the industry continues to consider copper access, while fiber is perceived as superior in terms of transmission properties.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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