Testing of WindFIRM/ForestGALES_BC: A hybrid-mechanistic model for predicting windthrow in partially harvested stands
Bibliographic record
Abstract
Windthrow is a common problem in forest management, particularly in areas exposed by recent harvesting or thinning. A hybrid-mechanistic model, WindFIRM/ForestGALES_BC, which builds upon the original ForestGALES, was developed to quantify component windthrow processes for individual trees in heterogeneous stands. The objectives of this work are to improve windthrow predictions at the tree level, represent the spatial patterns of windthrow and build a platform upon which new functions could be added in the future to improve the veracity of the model. This model accounts for irregular openings and is able to simulate the propagation of windthrow during storm events. Above canopy wind speed and direction are specified by the user or derived from spatial datasets. WindFIRM/ForestGALES_BC is integrated with a growth and yield model, TASS (Tree and Stand Simulator), which supplies spatial tree-lists. WindFIRM/ForestGALES_BC was tested using field plot data from the STEMS (Silvicultural Treatments for Ecosystem Management in the Sayward) research installation on Vancouver Island. The pattern of simulated windthrow is consistent with patterns observed in the field. Relative damage rates across tree size classes are also consistent with field plot data. Further refinements to WindFIRM/ForestGALES_BC which accounts for windthrow factors such as tree acclimation and resistance functions which account for site variability related to soils and root structure are suggested to improve predictions. However, the current model still provides insights into the consequences of cutblock design, and is a flexible platform for integration of new research on windthrow component processes.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".