An inventory-based approach for modeling single-tree storm damage — experiences with the winter storm of 1999 in southwestern Germany
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on individual tree damage data dating back to the gale “Lothar” (winter 1999) in Baden-Württemberg, Germany, a statistical model was developed to estimate the risk of storm damage for individual trees. The data were compiled from the National German Forest Inventory. The model attempts to separate the effects of tree-specific variables, topography, site conditions and flow field related effects on damage probability. The crucial problem of missing information on the actual flow field parameters was solved by applying a generalized additive model that enables the simultaneous fit of a spatial trend function. The geographical location of risk hotspots as predicted by the model correspond well to the actual distribution pattern of storm damage as assessed by the forest service. Tree height proved to be one of the most important factors affecting the level of damage, while height to diameter at breast height ratio influences damage probability to a much lesser extent. The Norway spruce ( Picea abies (L.) Karst.) group has the highest potential to be damaged followed by the silver fir ( Abies alba Miller) – Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) group and the Scots pine ( Pinus sylvestris L.) – larches ( Larix spp.) group. Predicted probabilities for deciduous trees are generally lower than those of conifers. West- to south-exposed locations bear a considerably higher damage risk and waterlogged soils show an increased predicted probability compared with slightly or not waterlogged soils.
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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 | 0.000 | 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