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Record W2805916283 · doi:10.1115/1.2012-dec-3

Storing Energy Underwater

2012· article· en· W2805916283 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

VenueMechanical Engineering · 2012
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCompressed air energy storagePumped-storage hydroelectricityCompressed airEnergy storageRenewable energyElectricityWind hybrid power systemsFlexibility (engineering)Wind powerAir compressorGas compressorEngineeringCapital costEnvironmental scienceWork (physics)Marine engineeringElectrical engineeringPower (physics)Mechanical engineeringDistributed generation

Abstract

fetched live from OpenAlex

This article discusses the advantage of compressed air energy storage (CAES) system. CAES has been proposed as an alternative to pumped hydro storage for large-scale, bulk energy management. CAES systems typically rely on electrically driven air compressors that pump pressurized air into large underground geological formations such as aquifers and caverns for storage. When the power is needed, turboexpanders connected to generators convert the compressed air back into electrical energy. Like pumped hydro, CAES can be scaled to sizes compatible for supplementing large renewable energy facilities. The lifetime costs for a CAES system can make it work as a means for storing cheap off peak electricity and selling it during peak hours, but capital costs and difficulties finding suitable geological structures have limited the technology’s applications. To make CAES more useful for storing wind-powered electricity, the systems have to become less expensive and have greater flexibility in sitting.

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 score0.899
Threshold uncertainty score0.828

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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