Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data
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
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 m 2 and 300 or 400 m 2 ). 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 (h L ), 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 h L 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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