Burial of downed deadwood is strongly affected by log attributes, forest ground vegetation, edaphic conditions, and climate zones
Bibliographic record
Abstract
Deadwood can represent a substantial portion of forest ecosystem carbon stocks and is often reported following good practice guidance associated with national greenhouse gas inventories. In high-latitude forest ecosystems, a substantial proportion of downed deadwood is overgrown by ground vegetation and buried in the humus layer. Such burial obfuscates the important process of deadwood carbon transfer to other pools (e.g., litter and soil) and emission to the atmosphere (i.e., rates of decay). Using data from the Swedish National Forest Inventory, we found that the proportion of downed logs that is buried increased from temperate to boreal forests. Several factors affect the probability of burial, including log attributes (e.g., decay class), ground vegetation (e.g., moss dominance, type of moss cover), and edaphic conditions (e.g., soil type, depth of organic layer). Combined assessments suggest that about 24% of the carbon in the aboveground downed deadwood pool was found to be buried in boreal forests. Deadwood burial has important implications for forest carbon dynamics and associated monitoring (e.g., United Nations Framework Convention on Climate Change reporting) as such a pool typically decomposes much slower compared with aboveground deadwood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".