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Record W4383768048 · doi:10.53829/ntr201906fa4

Carrier Edge Computing Infrastructure Technology for High-presence Virtual Reality Services

2019· article· en· W4383768048 on OpenAlexaff
Hiroki Iwasawa, Yusuke Urata, Shinya Tamaki, Kenta Kawakami, Yuuki NAKAHARA, Kotaro Ono, Ryota Ishibashi, Takeshi Kuwahara

Bibliographic record

VenueNTT technical review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAdvanced Computing and Algorithms
Canadian institutionsFuture Earth
Fundersnot available
KeywordsVirtual realityEnhanced Data Rates for GSM EvolutionServices computingComputer scienceEdge computingBusinessTelecommunicationsHuman–computer interactionWorld Wide WebWeb service

Abstract

fetched live from OpenAlex

Technical studies are underway at NTT Network Technology Laboratories on edge computing that uses computing resources in carrier facilities. This article introduces a technical demonstration of a use case providing a virtual reality application via an edge computing infrastructure performed in collaboration with Toppan Printing Co., Ltd.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

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 studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.015
GPT teacher head0.351
Teacher spread0.336 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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