Comparisons between field- and LiDAR-based measures of stand structural complexity
Why this work is in the frame
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Bibliographic record
Abstract
Forest structure, as measured by the physical arrangement of trees and their crowns, is a fundamental attribute of forest ecosystems that changes as forests progress through suc;cessional stages. We examined whether LiDAR data could be used to directly assess the successional stage of forests by determining the degree to which the LiDAR data would show the same relative ranking of structural development among sites as would traditional field measurements. We sampled 94 primary and secondary sites (19–93, 223–350, and 600 years old) from three conifer forest zones in western Washington state, USA, in the field and with small-footprint, discrete return LiDAR. Seven sets of LiDAR metrics were tested to measure canopy structure. Ordinations using the of LiDAR 95th percentile height, rumple, and canopy density metrics had the strongest correlations with ordinations using two sets of field metrics (Procrustes R = 0.72 and 0.78) and a combined set of LiDAR and field metrics (Procrustes R = 0.95). These results suggest that LiDAR can accurately characterize forest successional stage where field measurements are not available. This has important implications for enabling basic and applied studies of forest structure at stand to landscape scales.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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