Vuntut Gwitchin First Nation Community Energy Baseline Study
Bibliographic record
Abstract
A Community Energy Baseline (CEB) Study was carried out for the Vuntut Gwitchin First Nation (VGFN) in Old Crow, Yukon during the summer of 2005. The 2004 fiscal year was used as the base year for quantifying energy usage and subsequent greenhouse gas emissions. The goals of the CEB were: to examine the energy supply and demand; detail the sources and consumption; determine GHG emissions; and the financial cost of energy to the VGFN community, including both administrative and residential sectors. To determine an emISSIons benchmark, energy use findings were used to calculate greenhouse gas emissions from the VGFN during the 2004 fiscal year. The calculated total amount of energy utilized by the VGFN in 2004 was 24,100 GJ which resulted in the emission of 3,249 tonnes of neutral (biomass or wood) and non-neutral greenhouse gas emissions (C02 equivalent). The administrative sector used 17 % of all energy within the community and emitted 15 % of the greenhouse gases. The residential sector utilized 83 % of the energy and emitted 85 % of the greenhouse gases. The majority of energy use was relatively evenly split between the electrical, heating and transportation sectors. Diesel electrical generation was the leading contributor to greenhouse gas emissions. In addition to investigating the VGFN community, the entire community of Old Crow was also found to have utilized 33,687 GJ of energy and emitted 4,389 tonnes of eC02.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".