Combining interpolated maximum wind gust speed and forest vulnerability for rapid post-storm mapping of potential forest damage areas in Finland
Bibliographic record
Abstract
Abstract In Finland, wind-induced forest damage is expected to increase in the future. Demand exists for timely and precise first-hand information about the main impact area of windstorms. Locating potential damage areas quickly is essential for effective operational planning of salvage loggings, aiming to reduce monetary losses of timber and risk for secondary damage caused by insects. This study presents an approach for mapping the potential damage areas immediately after a windstorm, by using high-resolution forest vulnerability data and information on the spatial distribution of maximum wind gust speed derived from weather station observations using kriging with external drift interpolation. The new method is evaluated by analyzing damage caused by nine major windstorms of the 2010s in Finland. Our results show that including roughness length information as an auxiliary variable in the interpolation improved the results and produced wind maps with more plausible structure and better separation between forested and non-forested land areas. The forest vulnerability data were most strongly linked to damage, whilst wind gust speed had weaker results. However, for future storms with unknown damage areas, we consider maximum wind gust speed still essential for defining the main impact area, whereas forest vulnerability data could then be used for more detailed damage predictions. Further advancements of wind interpolation approaches, preferably towards higher resolution and, if possible, based on a denser and more diverse observation network, is needed to fully exploit the potential of combined wind and forest vulnerability data. Albeit we recognize multiple uncertainties, room for improvements and benefits that additional data sources would bring, our study demonstrates a simple approach for rapid mapping of potential forest wind damage areas, which could be further developed into an operational tool.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".