More Than One Option: An Integrated Approach to Reduce Diesel Reliance – Introducing Forest Biomass Energy in Old Crow, Yukon, a Pilot Project
Bibliographic record
Abstract
This paper aims to implement an integrated approach to renewable energy solutions for remote Indigenous communities in Canada, focusing on forest biomass as a sustainable alternative. Indigenous communities, disproportionately affected by climate change due to historical displacement and colonial relocation, are actively seeking alternatives to fossil fuels amidst Canada's goal of achieving net-zero emissions by 2050. However, existing climate regulations often overlook the unique challenges faced by these communities. A comprehensive cost-benefit analysis was conducted on the implementation of a woody biomass combined heat and power system to generate electricity in the Vuntut Gwitchin Nation, a remote community located in Old Crow, Yukon. The objective is to combine forest biomass-based energy with existing solar infrastructure to further reduce diesel reliance, improve community health, and address energy security. This research showcases the importance of a multi-pronged approach to renewable energy solutions for remote Indigenous communities and highlights the potential of biomass integration to achieve sustainable energy goals in Canada. It also suggests the need for inclusive climate policies and government support to ensure remote Indigenous communities have equitable access to clean energy transitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".