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Record W3208089148 · doi:10.32920/ryerson.14644833.v1

Comparing the Environmental Impacts of Diesel Generated Electricity with Hybrid Diesel-Wind Electricity for off grid First Nation Communities in Ontario : Incorporating a Life Cycle Approach

2021· preprint· en· W3208089148 on OpenAlexaboutno aff
Jade Schofield

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsDiesel fuelEnvironmental scienceWind powerElectricityLife-cycle assessmentTurbineElectricity generationAutomotive engineeringEnvironmental economicsEnvironmental engineeringEngineeringProduction (economics)EconomicsElectrical engineering

Abstract

fetched live from OpenAlex

The cost of diesel is rapidly increasing and the environmental impacts associated with diesel fuel combustion are substantial. Hybrid diesel-wind energy was found to be a feasible energy alternative for off-grid electricity production in seven First Nation communities of Ontario. Based on calculating the wind energy potential for a proposed 250 KW wind turbine and determining the amount of diesel that the wind turbine could replace hybrid diesel-wind has the potential to reduce diesel consumption and environmental impacts associated with the current diesel energy systems by 12-46% depending on the wind energy potential. Results of a life cycle analysis comparing the environmental impacts of the proposed hybrid diesel-wind system to the diesel system through the use of GaBi software show that global warming potential is the largest impact for both energy systems, but hybrid diesel-wind can significantly reduce the overall environmental impact caused by off grid diesel electricity generation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.247
Teacher spread0.200 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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