Duration of Climate Change Mitigation Benefits from Increasing Boreal Forest Harvest Age by 10 Years
Bibliographic record
Abstract
We presented a case study and associated method for stand scale assessment of the duration of the climate change mitigation benefit provided by increasing forest harvest age (i.e., the age a stand is harvested). We used stand yield curves and newly developed equations to estimate carbon stocks in various boreal forest ecosystem pools in Ontario, Canada. The proposed method was applied to forest identified as available for harvesting in management plans for three forest management units with a combined area of more than 1900 km2. Our analysis indicated that a 10-year increase in harvest age did not provide a mitigation benefit (reduced carbon stock) in about half the available harvest area (45.5%, 61.9%, and 62.1% of the total available harvest area in the management units). Increasing the harvest age by 10 years resulted in a mitigation benefit lasting longer than 25 years for 15.1%, 16.0%, and 13.0% of the total available harvest area in the management units. The results suggest that increasing harvest age may have limited mitigation potential in Ontario’s managed boreal forests in the short-term but can reduce overall carbon stocks in the longer term.
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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 | 0.001 |
| 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".