Estimation of standing dead tree class distributions in northwest coastal forests using lidar remote sensing
Bibliographic record
Abstract
The amount and variability of living and dead wood in a forest stand are important indicators of forest biodiversity, as it relates to structural heterogeneity and habitat availability. In this study, we investigate whether light detection and ranging (lidar) can be used to estimate the distribution of standing dead tree classes within forests. Twenty-two field plots were established in which each stem (DBH >10 cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal regression using a negative log–log link function was then employed to predict the cumulative proportions of stems within the WT classes. Results indicated that the coefficient of variation of the lidar height data was the best predictor variable (χ 2 = 106.11, p < 0.00; Wald = 4.83, p = 0.028). The derived relationships allowed for the prediction of the cumulative proportion of stems within WT classes (r = 0.90, RMSE = 6.0%) and the proportion of dead stems within forest plots (r = 0.61, RMSE = 16.8%). Our research demonstrates the capacity of lidar remote sensing to estimate the relative abundances of standing living and dead trees in forest stands and its ability to characterize vegetation structure across large spatial extents.
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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 | 0.001 |
| 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".