Testing an individual tree wind damage risk model in a naturally regenerated balsam fir stand: potential impact of thinning on the level of risk
Bibliographic record
Abstract
Abstract Widely distributed in Quebec, balsam fir (Abies balsamea (L.) Mill.) is highly vulnerable to wind damage. Recently, there has been a trend in forest management to increase the use of partial cuttings in naturally regenerating stands, leaving the remnant trees at increased risk of wind damage. In order to limit wind damage after partial cuttings, it is therefore important to find silvicultural practices that minimize the risk of wind damage in these fir stands. Our main objective was to find balsam fir-specific values of parameters to integrate into the wind risk model ForestGALES, in order to simulate the impact of different types of commercial thinning on wind damage risk, and to determine which practice potentially minimizes the risk in a naturally regenerated stand. An anemometer placed at canopy height and strain gauges attached to the trunks of balsam firs allowed us to measure the wind-induced bending moments experienced by a sample of balsam fir trees. This enabled the calculation of the turning moment coefficients specific to each of the trees in order to compare them with the ForestGALES model predictions and to adapt the model for balsam fir stands. The model was tested first with only tree diameter and height as input variables to calculate the turning moment coefficient, then with the addition of a competition index, and finally with the addition of crown dimensions. Wind climate parameters for prediction of the probability of damage were calculated using the Wind Atlas Analysis and Application Program airflow model. The model with the highest accuracy was then used to simulate two types of thinning and determine the impact on wind damage risk for each tree in the stand. According to the model’s predictions, thinning from below has a reduced risk of wind damage compared with thinning from above.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".