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Record W2103040155 · doi:10.1109/mnet.2009.4804332

Wireless data traffic: a decade of change

2009· article· en· W2103040155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Network · 2009
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceComputer networkMunicipal wireless networkWirelessWireless networkFixed wirelessInteroperabilityWireless WANWi-Fi arrayPopulationTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

This article presents an overview of the most significant changes in wireless data traffic and its main driving forces throughout the past decade (1998-2008). The main axes of change are discussed: wireless technology, user population, and applications. Evolution of wireless technology realized a 200-fold increase in data rate, and facilitated a continuous enrichment of the traffic mix traversing legacy and modern wireless networks. New applications emerged, such as peer-to-peer file sharing, online gaming, and multimedia, establishing a trend of significant increase in traffic volume. User population has also increased and even saturated some markets. However, not all benefits of wireless technologies are equally exploited, with only a few users exercising high mobility or regularly enjoying multimedia services. Future indications include a requirement for either integration or interoperability of two mainstream wireless technologies, WiFi and cellular, as well as continuous user demand for more bandwidth, broader coverage, and better mobility support.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.101
GPT teacher head0.316
Teacher spread0.215 · 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