More frequent burning increases vulnerability of Alaskan boreal black spruce forests
Bibliographic record
Abstract
Much recent research has investigated the effects of burning on mature black spruce ( Picea mariana ) forests in interior Alaska, however little research has focused on how frequent reburning affects soil organic layer (SOL) vulnerability in these ecosystems. We compared organic soil layer characteristics in black spruce stands that burned after two fire-free intervals (FFI), including an intermediate-interval (37–52 years) and a more typical long-interval (70–120 years). We found that depth of burn varied significantly between intermediate-interval and long-interval sites, and as there was less material available to burn in intermediate-interval stands, percent depth reduction was greater in these stands (78.9% ± 2.6%) than in long-interval stands (62.9% ± 1.5%). As a result, less residual organic soil carbon remained post-fire in intermediate-interval than long-interval stands. Post-fire organic soil carbon stocks averaged 0.51 ± 0.08 kg C m −2 in the intermediate-interval sites, which is less than estimates of soil carbon stock for long-interval fire events (ranging from 2.07 to 5.74 kg C m −2 ). In addition to altering soil carbon storage, a depletion of the SOL during more frequent fire events will likely delay the recovery of permafrost and could trigger a change in the possible successional trajectory of a site, from black spruce dominated to deciduous or even shrub dominated ecosystems in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".