MétaCan
Menu
Back to cohort
Record W2915843629 · doi:10.1109/mpe.2018.2884112

Distributed Generation and Megacities: Are Renewables the Answer?

2019· article· en· W2915843629 on OpenAlexaff
Vaclav Smil

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.

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)0.001
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0040.013
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0170.003

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

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

Citations28
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Power and Energy MagazineSame topicSmart Grid Energy ManagementFrench-language works237,207