Early indications of drought impacts on forests in the southeastern United States
Bibliographic record
Abstract
In the southeastern United States, drought can pose a significant threat to forests by reducing the amount of available water, thereby stressing trees. Destructive changes in crown conditions provide the first visible indication of a problem in a forested area, making it a useful indicator for problems within an ecosystem. Forest Health and Monitoring (FHM) and Palmer's Drought Severity Index (PDSI) data from 11 states in the southeastern United States were obtained in an effort to determine the role that drought, forest type, and ecoregion have in indicating differences in crown dieback. Analyses were conducted by species groups using classification and regression tree (CART) analysis. The greatest amount of total relative crown dieback occurred in red oak (18%), followed by other hardwoods (14%), and white oak (11%). Relative crown dieback varied by forest type and ecoregion with a relationship to drought in both red oak and white oak. This information will be useful for focusing future research and modeling efforts to predict forest health conditions affected by changing climate variables.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".