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The Emergence of Low Carbon Energy Autonomy in Isolated Communities

2013· article· en· W2133785172 on OpenAlexvenueno aff
Callum Rae, Fiona F. Bradley

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyEnergy supplyEnvironmental economicsEnergy (signal processing)Process (computing)BusinessEnergy policyEconomic systemNatural resource economicsEnvironmental resource managementEconomicsEngineeringPolitical scienceRenewable energyComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

This study examines the concept of switching from a centralised energy supply model towards a more autonomous model based on the use of low carbon technologies, from the viewpoint of isolated communities in the industrialised world. The study begins by establishing the importance of isolated communities within the field of energy research, and examining the concept of low carbon energy autonomy. It then analyses a number of exemplary case studies from across Europe, all of which have adopted (or are in the process of adopting) a highly autonomous energy supply model based on the use of low carbon technologies. The communities studied exhibit many of the theoretical challenges and opportunities associated with low carbon energy autonomy, including the potential for stimulating socio-economic development. They also highlight the need for a supportive and structured policy framework and more transparent routes to project funding, in order to lessen the reliance for the success of such projects upon motivated community groups. The role of academia and its relationship with industry was found to be important and the findings call for much greater transparency and knowledge sharing between key stakeholders to facilitate increased development and deployment of low carbon energy autonomy in the future.

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 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 score0.712
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.202
Teacher spread0.196 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2013
Admission routes1
Has abstractyes

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