An empirical financial analysis of integrating biomass procurement in sawtimber and pulpwood harvesting in eastern Canada
Bibliographic record
Abstract
Integrating forest biomass procurement in wood procurement for conventional products is a potential means of reducing bioenergy system supply costs. We studied forest harvest operations to procure biomass in the form of trees and tree sections, along with sawtimber and pulpwood. We evaluated the cost-effectiveness of the supply chain with a particular focus on harvest costs and the potential silvicultural savings from a reduced need for site preparation and reforestation. We compared four wood procurement scenarios of increasing intensity (from harvesting only sawtimber to harvesting sawtimber, pulpwood and biomass for bioenergy) at three sites in eastern Canada. Wood procurement intensity did not affect feedstock unit costs (CAD·m−3). At the stand scale, procuring biomass for bioenergy had limited impact on harvest costs (CAD·ha−1) for low-density stands with large trees. In contrast, high-density stands with small trees generated more feedstock for bioenergy (up to 50 m3·ha−1), but biomass procurement increased harvest costs. The cost-effectiveness of the wood supply chain did not vary significantly along the wood procurement intensity gradient we studied when silvicultural savings are considered. The proportion of biomass harvested, stand characteristics, and market conditions proved to be important factors that influence wood procurement profitability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".