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Record W4319781863 · doi:10.2172/1923455

Reservoir Lining for Pumped Storage Hydropower: Scoping Study of Geomembrane Lining Systems

2023· report· en· W4319781863 on OpenAlexaff
Jason Hedien, Mustafa S. Altinakar, Scott DeNeale, Vladimir Koritarov

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsStantec (Canada)
FundersOak Ridge National LaboratoryWater Power Technologies OfficePacific Northwest National LaboratoryArgonne National LaboratoryUT-BattelleBattelleUniversity of ChicagoU.S. Department of CommerceU.S. Department of Energy
KeywordsEnergy storagePiston (optics)Compressed airVolume (thermodynamics)Head (geology)Petroleum engineeringEnvironmental sciencePumped-storage hydroelectricityEngineeringGas compressorRenewable energyMechanical engineeringGeologyElectrical engineeringPower (physics)Distributed generation

Abstract

fetched live from OpenAlex

In April 2019, WPTO launched the HydroWIRES Initiative * to understand, enable, and improve the contributions of hydropower and pumped storage hydropower (PSH) to reliability, resilience, and integration in the rapidly evolving U.S. electricity system.The unique characteristics of hydropower, including PSH, make it well suited to providing a range of storage, generation flexibility, and other grid services to support the cost-effective integration of variable renewable resources.The U.S. electricity system is rapidly evolving, bringing both opportunities and challenges for the hydropower sector.While increasing deployment of variable renewables such as wind and solar have enabled low-cost, clean energy in many U.S. regions, it has also created a need for resources that can store energy or quickly change their operations to ensure a reliable and resilient grid.Hydropower (including PSH) is not only a supplier of bulk, low-cost, renewable energy but also a source of large-scale flexibility and a force multiplier for other renewable power generation sources.Realizing this potential requires innovation in several areas: understanding value drivers for hydropower under evolving system conditions, describing flexible capabilities and related tradeoffs associated with hydropower meeting system needs, * Hydropower and Water Innovation for a Resilient Electricity System ("HydroWIRES") optimizing hydropower operations and planning, and developing innovative technologies that enable hydropower to operate more flexibly.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.104
GPT teacher head0.345
Teacher spread0.241 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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