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Record W1970907646 · doi:10.1109/mnet.2015.7064896

Mobile cloud computing in 5G: Emerging trends, issues, and challenges [Guest Editorial]

2015· article· en· W1970907646 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 · 2015
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsWestern University
Fundersnot available
KeywordsMobile cloud computingCloud computingComputer scienceMobile computingMobile deviceServerMobile technologyThe InternetTelecommunicationsMultimediaComputer networkWorld Wide Web

Abstract

fetched live from OpenAlex

Mobile computing, wireless networks, and cloud computing are three technologies that are converging into a rapidly growing field of Mobile Cloud Computing (MCC). With the dramatically increased capacities of the 5th generation (5G) mobile networks, MCC services are expected to witness a period of rapid development and become a new hotspot of mobile services. It is anticipated that people's work patterns and life styles in a future networked society will be dramatically changed by MCC. IBM predicts that there will be 1 trillion cloud-ready devices by 2015, and most Internet users will work primarily through cyberspace-based applications on remote servers accessed through networked devices. Through future applications enabled by 5G, MCC will have profound impacts on almost every aspect of our future digital lives.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.053
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.279
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