Simulations of the influence of clear-cutting on the risk of wind damage on a regional scale over a 20-year period
Bibliographic record
Abstract
The aim of this study was to integrate component models for tree growth, wind damage, and airflow to assess the consequences of alternative forest-management options on the long-term risk of wind damage on a regional scale. This work could help forest managers to identify possible vulnerable edges and determine the probability of risk for alternative management plans. This new, integrated system was applied to assessing the risk of wind damage over a 20-year period on three alternative management choices. The risk was compared for the current forest edges without creating new edges (case study I) and situations where new edges were created through different clear-cut options (case studies II and III). Case study II represented more intensive cuttings compared with case study III (over four times more timber was cut). It was found that despite intensive cuttings in case study II, only 15% and 7% fewer vulnerable edges were found on average (risk probability class ≥0.1%) in case studies I and III, respectively. Therefore, forest managers must consider the possible risk of wind damage when harvesting timber.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".