Long-term snag and downed woody debris dynamics under periodic surface fire, fire suppression, and shelterwood management
Bibliographic record
Abstract
There are pronounced differences in the processes that act to determine the type and amount of standing and downed coarse woody debris present under partial harvesting versus other noncatastrophic disturbances. To evaluate long-term differences in snag and downed woody debris (DWD) dynamics, we developed a simulation model to project snag density and DWD volume by size and decay class in white pine ( Pinus strobus L.) and red pine ( Pinus resinosa Ait.) dominated stands under (i) a high-retention shelterwood system, (ii) periodic surface fire, and (iii) fire suppression. Snag densities under a high-retention shelterwood system were consistently lower than those in the fire-suppression and surface-fire scenarios, even if no large snags were felled at the time of harvest. Regular inputs from harvest residues were important in maintaining the total volume of DWD, but this material tended to be concentrated in a narrow range of decay classes at any given time. Preserving existing DWD at harvest was less influential than the level of inputs from harvest residues. Active measures for snag creation and staggering of harvest stages among adjacent stands may help minimize differences in the overall supply and temporal variation of coarse woody debris between managed and both naturally disturbed and old-growth stands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".