Woody Biomass Mobilization for Bioenergy in a Constrained Landscape: A Case Study from Cold Lake First Nations in Alberta, Canada
Bibliographic record
Abstract
Wood-based bioenergy systems developed and managed by Indigenous communities can improve their ability to thrive and grow economically and socially and improve their resource-based decision-making processes. In this study, we collaborated with Cold Lake First Nations (CLFN), a community located in Northern Alberta, Canada, to investigate the opportunities and challenges of biomass mobilization from different feedstocks. Based on remote sensing and ground data, harvest residue and fire residue feedstocks were identified within the boundaries of the community and inside a radius of 200 km at 18 and 39 oven-dry metric tonnes (odt)/ha, respectively. CLFN also received woody biomass from local oil and gas producers that operate in their traditional territory, which is estimated at 19,000 odt/year. Despite being abundant, the woody biomass is difficult to access due to the extensive human footprint that surrounds the area and constrains the landscape. In terms of greenhouse gas (GHG) mitigation, the potential also appears limited because the community has access to natural gas at a competitive and stable price, unlike off-grid communities. In terms of cost savings, the low oil and gas prices make the biomass resources (pellets) less competitive to utilize than the natural gas that is available in the community.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".