Assessing the potential impact of a biorefinery product from sawmill residues on the profitability of a hardwood value chain
Bibliographic record
Abstract
Due to the high amount of low-quality hardwoods harvested during selection cuts, the forest industry has been facing a decline in profit margins. One possible solution for utilizing the low-quality raw material is the production of extracts. The objective of this work was to estimate to what extent the inclusion of betulin in the traditional wood products portfolio could extend the profitability of a hardwood value chain. The profitability of a selection cut was assessed from the sawmill perspective, followed by an evaluation of the potential financial gain of producing betulin. Finally, the inclusion of betulin in a value chain was assessed. Results showed that the profitability of selection cuts was very low in some forest stands. The sensitivity analysis demonstrated that, among selected costs and revenues, profit was more sensitive to variations in the value of coproducts. If a fraction of coproducts volume was used to extract betulin, it would be sufficient to generate enough revenue to offset the total costs; however, a major constraint was the small size of the current betulin market, with annual sales not exceeding 1000 kg. Despite that, results demonstrate the potentially strong contribution of high value added extracts to the profitability of the forest value chain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".