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Record W2593484648 · doi:10.1109/mcom.2016.7470938

LTE-advanced pro: part 1 [Guest Editorial]

2016· article· en· W2593484648 on OpenAlexaff
Robert W. Heath, Michael L. Honig, Satoshi Nagata, Stefan Parkvall, Anthony C. K. Soong

Bibliographic record

VenueIEEE Communications Magazine · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsScience North
Fundersnot available
KeywordsComputer scienceTelecommunicationsMobile broadbandWirelessPenetration rateIMT AdvancedBroadbandMobile telephonyTerm (time)LTE AdvancedMobile computingMobile technologyComputer networkMultimediaMobile WebMobile radioTelecommunications linkEngineering

Abstract

fetched live from OpenAlex

Long Term Evolution (LTE) has become the most successful mobile wireless broadband technology, serving over one billion users as of the beginning of 2016. However, looking at the penetration rate, LTE serves only 14.5 percent of the current 7.3 billion mobile subscriptions. Consequently, there is still significant room for LTE to grow as a mobile technology; it will be serving users for a long time to come.

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)0.006
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0330.041

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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
GenreEditorial

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
Published2016
Admission routes1
Has abstractyes

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