Fire severity as a determinant factor of the decomposition rate of fire-killed black spruce in the northern boreal forest
Bibliographic record
Abstract
Several attributes might influence the decomposition process of fire-killed trees. Here, we tested various tree- and plot-level variables on the decomposition rate of fire-killed black spruce ( Picea mariana (Mill.) BSP) in the northern boreal forest. Data were collected from 474 individuals burned 17 years prior to sampling. Mean decomposition rate was relatively slow (k = 0.013) and was lowest for severely burned snags (k = 0.001) and highest for lightly burned logs (k = 0.027–0.036). Vertical position and fire severity were the most important variables influencing the decomposition rates, while plot-level variables were marginally significant. Both predictors strongly influenced the moisture content of fire-killed trees. Logs with greater contact with the ground and lightly burned trees had higher moisture content and faster decomposition rates. Very severely burned trees had lower moisture content because of faster bark shedding. This hampered the decomposition process by slowing the snag falling rate. Higher decomposition rates in lightly burned trees may result from greater colonization by early xylophagous species. By having a considerable impact on the decomposition of woody debris, fire severity may strongly influence many post-fire biological processes related to the woody necromass as well as carbon emission from burned stands.
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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 | 0.001 |
| 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".