Elevated mortality of residual trees following single-tree felling in northern hardwood forests
Bibliographic record
Abstract
In this paper, I examine tree mortality in northern hardwood forests subjected to single-tree felling. Mortality risk was estimated as a function of growth prior to harvest and time since harvest. This allowed me to separately quantify mortality due to (1) natural processes, (2) intentional felling, and (3) stress and (or) felling damage (postcut mortality). The long-term rate of mortality due to natural causes was 1.5% per year. The intentional felling of trees increased the average mortality to 3.1%, and postcut mortality of residual trees increased the average an additional 0.2%3.3% (the latter reflects both the direct negative effects of harvesting and the indirect positive effect of releasing residual trees from suppression). Thus, from a population-level perspective, the increased mortality caused by stress and (or) felling damage to residual trees was small compared to that caused by both natural mortality and tree felling. However, the long-term population average obscures more dramatic (though transient) effects of felling on small trees (DBH <10 cm), for which mortality rates increased by as much as 5% immediately after felling. This increase is several times greater than the natural mortality rate for small trees and may therefore have a substantial impact on stand structure and community dynamics.
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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 | 0.000 | 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) | 0.001 | 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".