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Record W2067875747 · doi:10.1109/mpe.2014.2317963

Northern Lights: Access to Electricity in Canada's Northern and Remote Communities

2014· article· en· W2067875747 on OpenAlexafffundabout
Mariano Arriaga, Claudio A. Cañizares, Mehrdad Kazerani

Bibliographic record

VenueIEEE Power and Energy Magazine · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsUniversity of Waterloo
FundersNatural Resources Canada
KeywordsElectrificationElectricityRenewable energyBusinessObstacleAgency (philosophy)Environmental economicsTelecommunicationsEngineeringGeographyEconomicsElectrical engineering

Abstract

fetched live from OpenAlex

Access to energy in many of the world's remote communities is still restricted; these locations only have access to simple and inexpensive local energy sources, such as biomass for cooking and kerosene lamps or candles for lighting. The World Bank and the International Energy Agency (IEA) perceive this energy deficit as a major obstacle to achieving community economic development as well as to obtaining adequate access to health services and clean water. Electricity is a flexible, modern source of energy that is considered to be one of the principal driving forces that stimulate community development and access to basic services in remote locations. Governments, private institutions, and nongovernmental organizations have gradually recognized these energy needs and have established electrification programs at the national and regional levels that aim at the gradual electrification of remote locations. The main objective is to give the reader a better understanding of the challenges and opportunities with regard to electricity generation in Canada's N&RCs beased on their use of renewable energy (RE) alternatives.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.200
Teacher spread0.194 · 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
GenreOther

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

Citations95
Published2014
Admission routes3
Has abstractyes

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