Long-Term Susceptibility of Even- and Uneven-Aged Northern Hardwood Stands to Partial Windthrow
Bibliographic record
Abstract
While uneven-aged silviculture may appear preferable to even-aged silviculture in terms of stand susceptibility to windthrow (major wind damage), the scientific evidence is equivocal on this issue, because the two systems do not operate over the same time frame. The goal of this study was to evaluate the windthrow susceptibility of even- and uneven-aged stands over a 100-year period. Susceptibility to windthrow of North American hardwood stands was evaluated by coupling a stand growth model (Forest Vegetation Simulator, or FVS) to stem windthrow probability equations from the literature. This coupling was straightforward given that FVS provides the diameter at breast height (DBH) of each tree within a stand over the simulation period. Windthrow susceptibility equations also use DBH to calculate stem windthrow probability. Our results show that average loss due to windthrow under uneven-aged management can be twice that observed under even-aged management at moderate wind severity for sugar maple-dominated stands. This result should be interpreted with caution because of the impossibility in our simulations of considering differences in tree form development between the two approaches. Nevertheless, this study clearly shows that even-/uneven-aged silviculture comparisons should be made on a long-term basis since uneven-aged stands are continuously susceptible to windthrow, while even-aged stands tend to be little affected by windthrow in their early developmental stages.
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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 | 0.000 | 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".