Interspecific variation in susceptibility to windthrow as a function of tree size and storm severity for northern temperate tree species
Bibliographic record
Abstract
Studies of wind disturbance regimes have been hampered by the lack of methods to quantify variation in both storm severity and the responses of tree species to winds of varying intensity. In this paper, we report the development of a new, empirical method of simultaneously estimating both local storm severity and the parameters of functions that define species-specific variation in susceptibility to windthrow as a function of storm severity and tree size. We test the method using data collected following a storm that struck the western Adirondack Mountains of New York in 1995. For intermediate-sized stems (e.g., 40 cm DBH), black cherry (Prunus serotina Ehrh.) and red spruce (Picea rubens Sarg.) showed the highest rates of windthrow across virtually all levels of storm severity, while yellow birch (Betula alleghaniensis Britt.) and sugar maple (Acer saccharum Marsh.) had the lowest rates of windthrow. For much of the range of storm severity, the probability of windthrow for the most susceptible species was at least twice as high as for the least susceptible species. Three of the species, yellow birch, red spruce, and beech (Fagus grandifolia Ehrh.), had significantly lower probability of windthrow at a given storm severity in old-growth stands than in second-growth stands. Our results suggest that the distinctive abundance of these three species in old-growth forests of the Adirondacks is due, at least in part, to their ability to survive the intermediate-scale disturbance events that appear to dominate the natural disturbance regime in this region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".