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Record W2792059393 · doi:10.1109/mpot.2017.2749118

RF Wireless Power Transfer: Regreening Future Networks

2018· article· en· W2792059393 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIEEE Potentials · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTelecommunicationsWirelessFootprintPhoneWireless networkBase stationWireless sensor networkEngineeringComputer networkCarbon footprintInformation and Communications TechnologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Over the past few years, green radio communication has drawn much attention from the research community, and it has a strong impact on various aspects, such as telecom businesses, wireless technologies, and natural environments. Specifically, the cost of electricity and CO<sub>2</sub> emissions have been increasing due to wireless network operation. For instance, the number of base stations (BSs) is more than 4 million, and each BS consumes an average of 25 MWh/year (an estimated approximate of 80% of the total network's power consumption). Bearing in mind the environmental perspective, generating sufficient power to supply the networks causes a significant CO<sub>2</sub> footprint. Particularly, the overall footprint of information and communication technology (ICT) services (e.g., computer, cell phone, and satellite networks) is predicted to triple by 2020.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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