Bioenergy potential from wood residuals in Alberta: a positive mathematical programming approach
Bibliographic record
Abstract
A major risk for many existing and planned wood-based bioenergy facilities is the uncertainty regarding future feedstock supply. Many bioenergy projects use waste generated from primary sectors such as lumber, and, therefore, carry the inherent risk of supply fluctuations if these industries change. To assess the long-term viability of a wood-based bioenergy facility, it is necessary to understand how biomass feedstock fluctuates with other sectors and at what cost supply can be made available. We address these issues by constructing a positive mathematical programming (PMP) model of the Alberta forest sector that focuses on optimizing fibre transfer routes. Through the use of PMP, we derive a marginal cost function for harvesting and hauling fibre to each processing facility. The results indicate that woody residual supply is quite sensitive to market conditions in the primary sector. For the most part, to support bioenergy expansion, feedstock will need to be sourced from the forest, as very few surplus mill residues are available even at high lumber prices. However, we estimate the marginal cost of delivering harvesting residues to be significant, which suggests that policy support will be needed for further bioenergy development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".