Long-term effects of fire frequency on carbon storage and productivity of boreal forests: a modeling study
Why this work is in the frame
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Bibliographic record
Abstract
Climate change is predicted to shorten the fire interval in boreal forests. Many studies have recorded positive effects of fire on forest growth over a few decades, but few have modeled the long-term effects of the loss of carbon and nitrogen to the atmosphere. We used a process-based, dynamic, forest ecosystem model, which couples the carbon, nitrogen and water cycles, to simulate the effects of fire frequency on coniferous forests in the climate of Prince Albert, Saskatchewan. The model was calibrated to simulate observed forest properties. The model predicted rapid short-term recovery of net primary productivity (NPP) after fire, but in the long term, supported the hypotheses that (1) current NPP and carbon content of boreal forests are lower than they would be without periodic fire, and (2) any increase in fire frequency in the future will tend to lower NPP and carbon storage. Lower long-term NPP and carbon storage were attributable to (1) loss of carbon on combustion, equal to about 20% of NPP over a 100-200 year fire cycle, (2) loss of nitrogen by volatilization in fire, equal to about 3-4 kg N ha(-1) year(-1) over a 100-200 year fire cycle, and (3) the fact that the normal fire cycle is much shorter than the time taken for the forest (especially the soil) to reach an equilibrium carbon and nitrogen content. It was estimated that a shift in fire frequency from 200 to 100 years over 1000 Mha of boreal forest would release an average of about 0.1 Gt C year(-1) over many centuries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it