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Record W2081036449 · doi:10.1109/mpe.2014.2301514

Powering Through the Storm: Microgrids Operation for More Efficient Disaster Recovery

2014· article· en· W2081036449 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

VenueIEEE Power and Energy Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsHydro-Québec
FundersLawrence Berkeley National LaboratoryU.S. Department of EnergyU.S. Department of Defense
KeywordsMicrogridDisaster recoveryElectricityNatural disasterMains electricityStormCompromiseEngineeringRisk analysis (engineering)Environmental economicsComputer scienceBusinessRenewable energyEconomics

Abstract

fetched live from OpenAlex

Disasters, whether natural or man-made, compromise the quality of life for all involved. In such situations, expeditious recovery activities are deemed imperative and irreplaceable for the restoration of normalcy. However, recovery activities rely heavily on the critical infrastructures that supply basic needs like electricity, water, information, and transportation. When disasters strike, it is likely that the critical infrastructures themselves are affected significantly, hampering efficient recovery processes, thus presenting a Catch-22 conundrum. In this article, we present examples from different parts of the world where distributed energy resources, organized in a microgrid, were used to provide reliable electricity supply in the wake of disasters, allowing recovery and rebuilding efforts to occur with relatively greater efficiency.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.409

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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