VALUING FOREST STAND AT A GLANCE WITH UAV BASED LIDAR
Bibliographic record
Abstract
Abstract. Comprehensive knowledge of characteristics and variability of any material is essential for its best utilization; hence forest managers are increasingly recognising the importance of resource quality characterisation earlier along the value-chain. Current methods for cruising timber at the stump lack information on branch characteristics and detailed assessment based on terrestrial lidar systems are restricted to sampled trees for assessment at the block level. Rich and dense information on vertical structure of the canopies captured using lidar system from a closer range like on a UAV platform provides a flexible, quick and a cost-effective alternative for assessing forest stands. In this study, along with detailed tree characterisation and variability, we explore the potential of ultrahigh density lidar data acquired from a UAV platform (ULS) to develop a non-destructive estimation of a suite of timber quality determinants like branchiness, clear stem and stem straightness for standing trees, and further determine possible amount of bucking segments (logs) and their expected quality. Validation of the algorithm is tested on white pine stand in Petawawa Research Forest, Ontario, Canada, holds promise in determining spatially-explicit tree level and hence stand quality.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".