Using crown condition variables as indicators of forest health
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
Indicators of forest health used in previous studies have focused on crown variables analyzed individually at the tree level by summarizing over all species. This approach has the virtue of simplicity but does not account for the three-dimensional attributes of a tree crown, the multivariate nature of the crown variables, or variability among species. To alleviate these difficulties, we define composite crown indicators based on geometric principles to better quantify the entire tree crown. These include crown volume, crown surface area, and crown production efficiency. These indicators were then standardized to a mean of 0 and variance of 1 to enable direct comparison among species. Residualized indicators, which can also be standardized, were defined as the deviation from a regression model that adjusted for tree and plot conditions. Distributional properties were examined for the three composite crown indicators and their standardized-residualized counterparts for 6167 trees from 250 permanent plots distributed across Virginia, Georgia, and Alabama. Comparisons between the composite crown indicators and their associated standardized residual indicators revealed that only two or three plots were jointly classified as poor by both when thresholds were set at the lower 5 percentiles of statistical distributions. In contrast, 19-21 other plots were classified differently, emphasizing that different aspects of crown condition are being summarized when the raw values are adjusted and standardized. Generally, crown volume and crown surface area behaved similarly, while crown production efficiency was substantially different.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it