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Record W4313525881 · doi:10.1109/mmm.2022.3210145

Radiative Wireless Power Transfer: Where We Are and Where We Want to Go

2023· article· en· W4313525881 on OpenAlexaff
Valentina Palazzi, Ricardo Correia, Xiaoqiang Gu, Simon Hemour, Ke Wu, Alessandra Costanzo, Diego Masotti, Enrico Fazzini, Apostolos Georgiadis, Hooman Kazemi, Ricardo Pereira, Naoki Shinohara, Dominique Schreurs, J.‐C. Chiao, Alexandru Takacs, Daniela Dragomirescu, Nuno Borges Carvalho

Bibliographic record

VenueIEEE Microwave Magazine · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsWireless power transferWirelessBattery (electricity)TelecommunicationsThe InternetElectrical engineeringPower (physics)Internet of ThingsComputer scienceRadiative transferEngineeringWorld Wide WebPhysics

Abstract

fetched live from OpenAlex

Providing energy to Internet of Things (IoT) apparatuses is ever more challenging. Humans are increasingly invested in the revolutionary new scenario that experts callIndustry 4.0, where billions of electronic devices are interconnected with one another. The operations of battery charging or battery replacement for these devices will soon become infeasible. As a consequence, radiative wireless power transfer (WPT) will soon become a leading technology as it is the only method available to support this technological revolution, and several information communication ­technology companies have already begun to actualize their interest in it.

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)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0060.014
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.006

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 head0.011
GPT teacher head0.212
Teacher spread0.200 · 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
GenreReview

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

Citations37
Published2023
Admission routes1
Has abstractyes

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