Characterizing Forest Succession in Central Ontario using Lidar-derived Indices
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
This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey's height (R 2 = 0.86; RSME = 2.36 m) and quadratic mean diameterat-breast-height (R 2 = 0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson's (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey's height index is optimal for separating early from mid succession stages (p <.0001) and a combination of height and complexity indices performed best to discriminate between mid- and late-succession stages (p <.0001).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it