Estimating windthrow risk in balsam fir stands with the Forest<i>Gales</i> model
Bibliographic record
Abstract
Balsam fir (Abies balsamea (L.) Mill.) forests are inherently vulnerable to windthrow, especially when silvicultural treatments are applied. During recent years, it has become possible to model windthrow risk based on a good understanding of windthrow mechanics. In the present paper, the British ForestGales model has been adapted for balsam fir with data from a winching study in Quebec, Canada. This model calculates the threshold wind speed required to break or overturn the average tree in a stand and then calculates the probability of exceeding the threshold value. Modifications of the equations predicting crown characteristics and overturning resistance were introduced. The effects of age, site quality, wind exposure, thinning and the creation of new edges were assessed. The estimated critical wind speed for overturning and breakage decreases with age but the probability of damage remains low on sheltered sites. The creation of a new edge leads to an increased probability of damage, especially on exposed, highly productive sites. Thinning alone also increases the probability of damage and the magnitude of the increase varies with age and thinning intensity. On highly productive sheltered sites, the effect of thinning becomes especially important when thinning exceeds 35% of the number of stems or when stand age is greater than 70 years for a 35% thinning intensity. Thinning of new edges was also found to further increase the risk of windthrow on the most sheltered, high quality sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".