Predicting tree damage in fragmented landscapes using a wind risk model coupled with an airflow model
Why this work is in the frame
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Bibliographic record
Abstract
Forest mechanistic wind risk models are widely applied on heterogeneous landscapes, whereas their wind load parameterizations are often derived either from homogeneous stand conditions or from simple forest edge conditions. To evaluate the impact of improving the wind flow representation of the mechanistic wind risk model HWIND on tree damage predictions when applied on heterogeneous environments, we coupled HWIND with the airflow model Aquilon. Aquilon provides to HWIND the velocity profiles and the gust factor (deduced from an approach based on the probability distribution of the wind velocity and on the turbulent kinetic energy). HWIND–Aquilon is compared with HWIND alone on different stand configurations of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) comprising newly clearcuts or shelter stands. Although both models showed the same pattern of differences in edge-tree critical wind speeds with differences in clear-cut length and shelter stand height, the model comparison reveals significant differences in the magnitude of critical wind speeds between them. This discrepancy is explained by the wind velocity and gust factor parameterizations used in HWIND alone, as in other wind risk models that exhibit weaknesses in heterogeneous configurations. This result confirms the need for improving the wind flow representation in mechanistic wind risk models when applied to heterogeneous landscapes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it