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Implications of microgrids, economic autonomy and renewable energy systems for remote Indigenous communities

2020· article· en· W3127620987 on OpenAlexaffabout
Alyssa A. Schatz, Petr Musı́lek

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousRenewable energyAutonomyNatural resource economicsBusinessEnvironmental resource managementEnvironmental economicsEconomicsEngineeringPolitical scienceEcology

Abstract

fetched live from OpenAlex

Indigenous knowledge has the capacity to facilitate the utilization of microgrids within Indigenous energy systems to spur reconciliation and self-determination. Since Indigenous ways of knowing can be conceptually related to renewable energy systems, ecological economic ideology and various forms of innovation, there is an organic opportunity for microgrids, amongst other decentralized energy technologies, to work in concert with Indigenous communities. The natural eclipse of theory and practice has the potential to uplift Indigenous communities across Canada and has already done so. Renewable energy systems enable Indigenous people to address local issues and promote autonomy. Additionally, through Indigenous ownership and the fundamental comprehension of the complexities that Indigenous people face, there is great potential to uplift these communities as forerunners within the renewable energy sector. By analyzing how microgrids can capture renewable generation, co-generation and Indigenous ownership, specifically though islanded microgrids, there is a great potential for further impact.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.007
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.018
GPT teacher head0.220
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

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 designObservational
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

Citations5
Published2020
Admission routes2
Has abstractyes

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