MétaCan
Menu
Back to cohort
Record W2187186932 · doi:10.1016/j.egypro.2015.11.494

Energy Storage: Technology Applications and Policy Options

2015· article· en· W2187186932 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

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsEnergy storageSoftware deploymentPumped-storage hydroelectricityRenewable energyEnergy supplyDecoupling (probability)SpinningEnvironmental economicsIntermittent energy sourcePeak demandDistributed generationComputer scienceEngineeringEnergy (signal processing)Electrical engineeringPower (physics)ElectricityOperating systemControl engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents technology applications and policy options related to energy storage in energy systems or grids. Energy storage technologies are promising tools to achieve a low-carbon future since they allow for the decoupling of energy supply and demand. Energy storage technologies could potentially be deployed across the supply, transmission, distribution and demand portions of an energy system or grid. The services they provide are either based on a power application or an energy application; and they range from long-term seasonal storage to short duration spinning and non-spinning reserves. In terms of energy storage technologies, pumped storage hydropower systems are a mature technology and comprise over 99% of the current total global installed capacity of energy storage technologies, which is evaluated at over 141 GW. In order to achieve widespread deployment, policy options should seek to enable compensation for the multiple services performed across the energy system.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.419

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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