Deriving Merchantable Volume in Poplar through a Localized Tapering Function from Non-Destructive Terrestrial Laser Scanning
Bibliographic record
Abstract
Timber volume is an important ecological component in forested landscapes. The application of terrestrial laser scanning (TLS) to volume estimation has been widely accepted though few species have well-calibrated taper functions. This research uses TLS technology in poplar (Populus × canadensis Moench cv. ‘I-72/58’) to extract stem diameter at different tree heights and establish the relationship between point cloud data and stem curve, which constitutes the basis for volume estimation of single trees and the stand. Eight plots were established and scanned by TLS. Stem curve functions were then fitted after extraction of diameters at different height, and tree heights from the point cloud data. Lastly, six functions were evaluated by R2 and RMSE. A modified Schumacher equation was the most suitable taper function. Volume estimates from the TLS-derived taper function were better than those derived using the stem-analysis data. Finally, regression analysis showed that predictions of stem size were similar when data were based on TLS versus stem analysis. Its high accuracy and efficiency indicates that TLS technology can play an important role in forest inventory 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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".