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Record W2345485679 · doi:10.1080/00207233.2016.1165479

Power-to-gas in a demand-response market

2016· article· en· W2345485679 on OpenAlex
Ushnik Mukherjee, Sean Walker, Michael Fowler, Ali Elkamel

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Environmental Studies · 2016
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPower to gasNatural gasEnergy storageEnergy carrierPower (physics)Demand responseElectrolysisProcess engineeringEnvironmental scienceWaste managementRenewable energyElectrolyteEngineeringChemistryElectrical engineeringElectricityPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Power-to-gas is a versatile and effective form of energy storage by which electrolysis generates hydrogen. The produced hydrogen thus becomes an alternative energy vector, which can be contained within the natural gas energy infrastructure or other storage medium. By means of the rapid response of polymer electrolyte membrane electrolysers, Power-to-Gas can also use natural gas energy storage to offer auxiliary and regulatory power services of high value, as well as energy transformation. A General Algebraic Modelling Simulation illustrates the effectiveness of Power-to-Gas in offering green hydrogen while also performing Demand-Response ancillary services.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.895

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)0.0010.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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