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Record W2113254970 · doi:10.1109/epec.2010.5697222

Stationary applications of energy storage technologies for transit systems

2010· article· en· W2113254970 on OpenAlexaff
P. Radcliffe, James S. Wallace, Lily H. Shu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlywheelEnergy storageTransit (satellite)Flywheel energy storageComputer scienceSystems engineeringEmerging technologiesEngineeringTransport engineeringAutomotive engineeringPublic transport

Abstract

fetched live from OpenAlex

Stationary energy storage technologies can improve the efficiency of transit systems. In this paper, three different demonstrations of energy storage technologies for transit systems were reviewed and discussed. The demonstrations reviewed were a sodium sulphide battery system in Long Island, a flywheel system for the London Underground, and a capacitor system for Madrid de Metro. Analysis was conducted from the point of view of a transit agency evaluating these demonstrations for practical lessons learned, effectiveness of the installation, and return on investment. Each installation showed that their technology was successful in their task and also provided valuable lessons on the challenges of implementing new technologies. Information from independent sources about longer demonstrations would be particularly valuable to help accelerate acceptance of energy storage technologies for transit systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations47
Published2010
Admission routes1
Has abstractyes

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