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Record W4366607838 · doi:10.1109/mdat.2022.3221909

Special Issue on Approximate Computing: Challenges, Methodologies, Algorithms, and Architectures for Dependable and Secure Systems

2023· article· en· W4366607838 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 Design and Test · 2023
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceCloud computingEnergy consumptionMobile broadbandBroadbandDistributed computingWirelessTelecommunicationsMobile cloud computingMobile deviceMobile computingElectricityComputer securityComputer networkWorld Wide WebEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The current trend in energy resources shapes how computing systems will address new challenges in the following years. As a matter of fact, following the current trend, by 2040, computers will require more energy than the world’s resources can generate. In the very upcoming future, by 2025, data centers alone will consume 20% of all available electricity. A similar trend already impacts the communications side where, for example, energy consumption in mobile broadband networks and mobile terminals is comparable to data centers. These trends can only accelerate by broadening the spectrum of possible mobile applications to the Internet of Things (IoT), which will connect 50 billion devices through wireless connections to the cloud infrastructure within a few years.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.921
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.103
GPT teacher head0.319
Teacher spread0.216 · 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