Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data
Bibliographic record
Abstract
Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.1 points·m–2 collected over 132 sample plots and 61 stands. Field measurements of each plot were carried out within two concentric circles (200 m2 and 300 or 400 m2). The plot positions were altered randomly with Monte Carlo simulations. For various metrics derived from the canopy height distributions, the mean and the standard deviation (SD) of the differences between incorrect plot positions and ground-truth positions were compared. In general, SD was smaller for large field plots than for small plots, and the variation in SD among the Monte Carlo repetitions was smaller for large sample plots. The combined effects of field plot size and sample plot position error on the accuracy of mean tree height (hL), stand basal area (G), and stand volume (V) predicted at stand level using a two-stage procedure combining field training data and laser data were assessed. Standard deviation of the differences between predicted and observed hL was quite stable and of similar size for position errors up to 5 m. However, for G and V the influence of plot position error was more pronounced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".