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Record W2915843629 · doi:10.1109/mpe.2018.2884112

Distributed Generation and Megacities: Are Renewables the Answer?

2019· article· en· W2915843629 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 · 2019
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRenewable energyDistributed generationElectricity generationElectricityElectricity retailingStand-alone power systemEnvironmental economicsElectric power transmissionMains electricityEngineeringElectricity marketComputer scienceElectrical engineeringVoltagePower (physics)Economics

Abstract

fetched live from OpenAlex

Distributed electricity generation is the opposite of centralized electricity production, the mode that has dominated modern commercial electrical supplies for more than a century. Rather than relying on large central stations (fossil fueled, nuclear, or hydro) and high-voltage transmission lines, distributed electricity generation depends on small-scale, decentralized, local, on-site generation, preferably by tapping renewable energy sources. This arrangement avoids long-distance transmission losses, and, once organized in a web of smart microgrids, its design improves supply stability and reliability and gives users more control. As the cost of new renewable energy conversions continues to decline, this form of electricity supply is expected to claim a rising share of overall generation. Indeed, according to Rodan Energy, distributed generation is not just the future of electricity, but also "the future of energy."

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: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.522

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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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