Biomass procurement in boreal forests affected by spruce budworm: effects on regeneration, costs, and carbon balance
Bibliographic record
Abstract
Biomass procured from forests affected by natural disturbances as a bioenergy source is increasingly considered in the context of climate change mitigation. By comparing clearcuts with and without biomass procurement, we aimed to determine the effects of biomass extraction performed alongside lumber harvesting on regeneration density, number of planting microsites, forest renewal costs, and carbon fluxes, in harvested boreal stands affected by spruce budworm. The results showed that biomass procurement increased regeneration density and number of planting microsites. Reduction of downed woody debris due to biomass procurement lowered site preparation costs by 282.07 $CAN·ha−1, equivalent to 14.45 $CAN per oven-dry metric ton (odmt−1) of harvested biomass. Product value from biomass processing had to reach from 13.90 to 76.84 $CAN·odmt−1 to make biomass procurement operations profitable. Since biomass procurement significantly increased stocking and reduced the amount of decaying debris, it also reduced cumulative CO2 emissions relative to scenarios without biomass procurement. However, ensuring forest renewal through site preparation and plantation per se, irrespective of biomass procurement, played a more important role for carbon sequestration and net balance. Integrating biomass harvesting with silviculture could have significant ecological and financial impacts on forest management while supporting mitigation efforts against climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".