Automated airborne lidar-based assessment of timber measurements for forest management
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
Received: 12 July 2012 Abstract Accepted: 30 August 2012 This paper presents processing and analysis techniques to apply LiDAR data to estimate tree diameter at breast height (DBH) – a critical variable applied in a large number of forest management tasks. Our analysis focuses on the estimation of DBH using only LiDAR-derived tree height and tree crown dimensions, i.e., variables accessible from aerial observations. The modeling process was performed using 161 white and red pine trees from four 3850 m plots in the Foret de l’Aigle located in southwestern Quebec. Segments of the LiDAR data extracted for DBH estimation were obtained using the Individual Tree Crown (ITC) delineation method. Regression models were investigated using height as well as crown dimensions, which increased the precision of the model. This study demonstrates that DBH can be modeled to acceptable accuracy using altimetry data and automated data processing procedures and then be used in high-precision timber volume assessment.
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 | 0.001 | 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 | 0.000 |
| 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