MétaCan
Menu
Back to cohort
Record W4315489554 · doi:10.1109/msmc.2022.3201365

Blockchain Technology in Modern Power Systems: A Systematic Review

2023· review· en· W4315489554 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 Systems Man and Cybernetics Magazine · 2023
Typereview
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBlockchainComputer scienceHash functionElectric power systemDistributed ledgerData sharingPeer-to-peerComputer securityDistributed computingPower (physics)

Abstract

fetched live from OpenAlex

Blockchain is a distributed decentralized peer-to-peer network, which is used for sharing data across a large number of entities in a trusted and secure way. Blockchain utilizes different mechanisms, such as hash functions, consensus algorithms, etc., for data verification and validation. In modern power systems, blockchain technology is used for balancing supply and demand, contributing to the demand-side management programs, and mainly, transitioning consumers to prosumers to trade electricity and reduce operational costs. This article aims at providing an in-depth discussion on energy transition and digitalization in power systems and investigating the role of blockchain technology in modern power systems. In addition, opportunities, challenges, and limitations of blockchain technology in modern power systems are discussed.

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)0.002
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.282
Teacher spread0.259 · 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