Dynamics of moisture content in spruce–feather moss and spruce–Sphagnum organic layers during an extreme fire season and implications for future depths of burn in Clay Belt black spruce forests
Bibliographic record
Abstract
High moisture levels and low frequency of wildfires have contributed to the accumulation of the organic layer in open black spruce (Picea mariana)–Sphagnum dominated stands of eastern boreal North America. The anticipated increase in drought frequency with climate change could lead to moisture losses and a transfer of the stored carbon back into the atmosphere due to increased fire disturbance and decomposition. Here we studied the dynamics of soil moisture content and weather conditions in spruce–feather moss and spruce–Sphagnum dominated stands of the boreal Clay Belt of eastern Canada during particularly dry conditions. A linear mixed model was developed to predict the moisture content of the organic material according to weather, depth and site conditions. This model was then used to calculate potential depth of burn and applied to climate model projections to determine the sensitivity of depth of burn to future fire hazards. Our results suggest that depth of burn varies only slightly in response to changes in weather conditions in spruce–Sphagnum stands. The reverse holds true in spruce–feather moss stands. In conclusion, our results suggest that spruce–Sphagnum stands in the boreal Clay Belt may be resistant to an increase in the depth of burn risk under climate change.
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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 | 0.000 |
| 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 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".