Snag dynamics in partially harvested and unmanaged northern hardwood forests
Bibliographic record
Abstract
We used data from hardwood-dominated permanent sample plots in Ontario to estimate the probability of a tree falling during the 5 year period in which it dies ("tree fall"), and likewise the 5 year probability of snag fall. Tree fall probabilities ranged from 5% to 31% across species, with smaller dead trees more likely to be downed than larger ones. Expected half-lives (median time from death to fall) for 25 cm diameter snags varied from 5 to 13 years among species. Fall rates were higher for 10 cm diameter snags but relatively constant for 20–60 cm diameter snags. Recent harvesting substantially increased the probabilities of both tree fall and snag fall, with the former effect most pronounced for small individuals. We used these estimated fall rates to simulate snag dynamics in uneven-aged sugar maple (Acer saccharum Marsh.) stands. Mean snag densities were 32 and 50 snags/ha in selection-management and old-growth scenarios, respectively. Fifty-four percent of this difference was attributable to the lower density of live trees in the selection-management scenario, while 31% was attributable to losses of snags during harvesting. Silvicultural practices that strategically increase tree mortality rates, together with snag retention, as far as safety permits, during harvesting, could represent an effective approach to snag management under the selection system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".