Degraded Trees from Spruce Budworm Epidemics as Bioenergy Feedstock: A Profitability Analysis of Forest Operations
Bibliographic record
Abstract
Natural disturbances are common in Canadian boreal managed forests. For example, during and after insect epidemics, foresters must deal with significant amounts of degraded or dead wood that cannot be processed into sawn timber or pulp. Bioenergy could be an alternative pathway for this wood. A case study in Quebec (Canada) was used to evaluate the profitability of pellet production for bioenergy using degraded trees from insect epidemics. A bioenergy scenario was simulated in which degraded trees were harvested for bioenergy alongside sound wood for timber and pulp. This scenario was compared to a reference scenario in which degraded trees were left on cutovers. Using wood pellets as a case study, the results showed that at current market prices, harvesting degraded trees for pellet production is not as profitable as leaving them in the forest. Nevertheless, the overall forest operations for procuring wood for timber and pulp were still profitable, even with very high degradation levels. Procuring degraded trees reduced the overall fixed costs per harvested m3 and allowed average savings of C$2.83/harvested m3. The silvicultural savings associated with lower site preparation needs following procurement of degraded trees ranged from C$0/ha to C$500/ha, resulting in average savings of C$2.31/harvested m3. Depending on the stand conditions, the distribution of fixed costs and silvicultural savings of biomass procurement could be either low or significant.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".