Mechanistic and statistical approaches to predicting wind damage to individual maritime pine (<i>Pinus pinaster</i>) trees in forests
Bibliographic record
Abstract
Maritime pine (Pinus pinaster Aiton) forests in the Aquitaine region, southwestern France, suffered catastrophic damage from storms Martin (1999) and Klaus (2009), and more damage is expected in the future due to forest structural change and climate change. Thus, developing risk assessment methods is one of the keys to finding forest management strategies to reduce future damage. In this paper, we evaluated two approaches to calculate wind damage risk to individual trees using data from different damage data sets from two storm events. Airflow models were coupled either with a mechanistic model (GALES) or a bias-reduced logistic regression model to discriminate between damaged and undamaged trees. The mechanistic approach was found to successfully discriminate the trees for different storms but only in locations with soil conditions similar to where the model parameters were obtained from previous field experiments. The statistical approach successfully discriminated the trees only when applied to similar data as that used for creating the models, but it did not work at an acceptable level for other data sets. One variable, decade of stand establishment, was a significant variable in all statistical models, suggesting that site preparation and tree establishment could be a key factor related to wind damage in this region.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".