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Record W2502062967 · doi:10.36479/jhe.v4i2.57

The design and installation of Solar Home Systems in rural Cambodia

2016· article· en· W2502062967 on OpenAlexaff
Rebecca Alice Watts, Jeremy Smith, Andrew Thomson

Bibliographic record

VenueJournal of Humanitarian Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsEngineers Without Borders Canada
Fundersnot available
KeywordsElectricityBusinessRural areaSoftware deploymentSolar energyKeroseneEconomic growthEnvironmental economicsEngineeringEconomicsPolitical science

Abstract

fetched live from OpenAlex

This study contends that solar home systems (SHS) are an appropriate solution to provide affordable, reliable and clean electricity in rural Cambodia. SHS provide decentralised electricity suitable for the electricity needs of rural households and with the decreasing cost of solar energy technologies, SHS are becoming an increasingly competitive source of energy. This study details the design and installation of two SHS in a rural community in Cambodia. The SHS have replaced the use of kerosene lamps and supplemented car battery usage, which has generated a cost saving of USD$2.50-3.20 per month. The SHS have increased the hours of quality lighting making it possible for users to improve educational outcomes by studying at night and participating in private education classes as well as potentially extending their working hours that provides an opportunity to increase their income. Community involvement in the installation of SHS and participation in an education program has ensured transfer of knowledge about system operation and maintenance at a local level that has ensured economic, social and environmental were benefits. This study builds a case in support of solar energy at the household level in rural Cambodia and makes recommendations for the deployment of SHS in rural communities throughout the developing world.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.131

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 head0.165
Teacher spread0.158 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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