Carbon Uptake and Forest Management under Uncertainty: Why Natural Disturbance Matters
Bibliographic record
Abstract
This study examines how natural disturbance can adversely affect the carbon sequestration potential of the forest, and the potential contribution that genomics might make towards offsetting these impacts when carbon is priced. A stochastic dynamic programming model of the BC interior, which includes a detailed carbon accounting module, shows that harvests are delayed as carbon prices rise, with less carbon stored in harvested wood products and more in the forest ecosystem, but an increase in the risk of natural disturbance causes the landowner to harvest sooner. As natural disturbance increases in prevalence and severity, this will somewhat offset the lengthening of rotation age that occurs when carbon is priced. With disturbance, the total amount of carbon sequestered falls significantly, but some of this can be recovered through proactive planting of genetically modified (GM) stems that are more productive and less susceptible to disturbance. To make such an investment worthwhile, however, the costs of planting GM stock should not exceed $120–$150/ha. Finally, this study suggests that a modest price of carbon (somewhat less than $25/tCO2) can be an effective incentive to encourage land owners to reduce the rotation age brought about by disturbance, and generate additional carbon offsets.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".