Carbon balance of forest management and wood production in the boreal forest of Quebec (Canada)
Bibliographic record
Abstract
Management of boreal forests can increase terrestrial carbon sinks and reduce greenhouse gas (GHG) emissions to the atmosphere. A case study was conducted in the boreal balsam fir forests of Quebec (Canada), a commercially important region for forestry, to identify optimal management and wood production solutions that contribute to reducing GHG emissions to the atmosphere. Scenarios were based on a steady level of harvest and silvicultural activities over time and a stable flow of wood products to markets. Scenarios included: increases and decreases in the volume of harvested timber; the transition of harvesting activities from clearcuts (the most common practice in the region) to partial cuts; and changes in the rate of natural regeneration (the usual mode of regeneration) vs. plantations. All scenarios provided a carbon sink regardless of the time frame. Compared with other scenarios, reducing harvest levels increased the forest carbon sink in the short (10 to 20 years) and medium (20 to 50 years) terms. Also, for a similar harvest level, the increased use of partial cutting and planting increased the forest carbon sink. In the long term (over 50 years), strategies with low harvesting levels resulted in lower ecosystem carbon sequestration, even though they still had the lowest cumulative emissions. Nevertheless, higher harvesting levels could not be justified because the long-term increase in the forest ecosystem carbon sink could not offset higher emissions from wood products, particularly from short-lived paper products. Sensitivity analyses showed that improving sawmill efficiency and thus increasing the proportion of long-lived products was an important factor that can greatly reduce emissions. On the other hand, transportation distances between forest stands and sawmills had a relatively marginal impact on the overall carbon balance of forest management and wood production scenarios.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".