Decomposition differences between snags and logs in forests of Kenai Peninsula, Alaska
Bibliographic record
Abstract
Rising temperatures could increase forest ecosystem disturbance frequency and severity, transferring large amounts of live biomass to coarse woody debris (CWD) pools that emit carbon (C). The type of disturbance-created CWD influences the rate, amount, and duration of this C emission. We studied the effect of a large-scale disturbance on CWD dynamics in spruce-dominated forests of the Kenai Peninsula, Alaska, by determining CWD decomposition rate constants (k) using the chronosequence and decomposition-vectors methods and by modeling the hypothetical CWD dynamics occurring after a bark beetle outbreak versus a windthrow. Chronosequence-based k’s for mass ranged between 0.020 and 0.022 year−1 for logs and between 0.000 and 0.003 year−1 for snags. Decomposition-vectors-based k’s for log mass ranged between 0.022 and 0.045 year−1 among three decomposition phases and between 0.014 and 0.048 year−1 among five decay classes. Our analysis showed that snag-generating disturbances delayed C flux from CWD to the atmosphere, produced a smaller magnitude of C flux, and had the potential to store 10% to 66% more C in the system over time than disturbances generating logs. Thus, landscapes affected by disturbances creating snags (versus logs) may revert faster to C neutrality, suggesting forest management practices should reflect these differences.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".