Within-stand variation in windthrow in southern boreal forests of Minnesota: Is it predictable?
Bibliographic record
Abstract
Wind damage to forests is determined by numerous factors that interact to produce complex, seemingly random damage patterns. However, the complexity may lie mostly among stands and be less within stands: in this study, I attempted to discern how predictable tree fall risk is within five southern boreal forest stands in northeastern Minnesota. I sampled five stands in the Boundary Waters Canoe Area Wilderness, following a July 1999 catastrophic windstorm. Levels of damage varied from 29.5% to 86.8% of basal area fallen and 23.3% to 63.4% of stems fallen. In all sites, the disturbance reduced mean trunk diameter of standing trees. In general, Abies balsamea (L.) Mill. was the most vulnerable species. I split the data set from each site into predictor and test portions and used the predictor data sets to derive logistic regression parameters for the relationship of tree size (trunk diameter) to probability of tree fall. Models based on these parameters allowed quite accurate predictions of the levels of damage in the test portion of each stand. For the five sites, the proportion of test trees predicted to fall differed from the proportion observed to fall by 5.7%, 3.9%, 8.3%, 1.4%, and 3.7% of the total test sample size. This suggests that while numerous factors indeed influence tree fall risk, the sizes and identities of trees may account for most of the within-stand variation in damage.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".