Wind damage over 21 years across different levels of tree removal in natural-origin mixed forests of northwestern British Columbia
Bibliographic record
Abstract
In many regions, forestry practices are shifting to partial harvesting approaches that seek to maintain species and structural diversity in managed forests. We monitored windthrow for 21 years following partial cutting treatments with 0%, 30%, and 60% removal in a large, replicated experiment located in mixed-species mature and old-growth forests of fire origin. There was no evidence that wind damage to merchantable trees (≥17.5 cm) varied among the three removal treatments. We found no evidence of a short-term spike in susceptibility to windthrow after partial cutting during the initial years following treatment. Over 21 years, a total basal area of 2.4 m2·ha–1 was damaged, which was 5.9% of the original standing basal area at the start of the experiment. We found clear differences in susceptibility to windthrow among the different tree species. The percentage of original standing trees that were windthrown varied from 0% to 23.7%. Eight of nine species had ≤10% damage over the monitoring period. Foresters should be aware of differences among tree species in risk of wind damage but should not use a general concern about susceptibility to windthrow as a reason to avoid partial cutting systems (that can achieve a diversity of management objectives) in structurally diverse, multispecies forests.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".