Could increased boreal forest ecosystem productivity offset carbon losses from increased disturbances?
Bibliographic record
Abstract
To understand how boreal forest carbon (C) dynamics might respond to anticipated climatic changes, we must consider two important processes. First, projected climatic changes are expected to increase the frequency of fire and other natural disturbances that would change the forest age-class structure and reduce forest C stocks at the landscape level. Second, global change may result in increased net primary production (NPP). Could higher NPP offset anticipated C losses resulting from increased disturbances? We used the Carbon Budget Model of the Canadian Forest Sector to simulate rate changes in disturbance, growth and decomposition on a hypothetical boreal forest landscape and to explore the impacts of these changes on landscape-level forest C budgets. We found that significant increases in net ecosystem production (NEP) would be required to balance C losses from increased natural disturbance rates. Moreover, increases in NEP would have to be sustained over several decades and be widespread across the landscape. Increased NEP can only be realized when NPP is enhanced relative to heterotrophic respiration. This study indicates that boreal forest C stocks may decline as a result of climate change because it would be difficult for enhanced growth to offset C losses resulting from anticipated increases in disturbances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".