Impact of stem lean on estimation of Douglas-fir (<i>Pseudotsuga menziesii</i>) diameter and volume using mobile lidar scans
Bibliographic record
Abstract
The value of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), which is the predominant commercial species in the Pacific Northwest, depends on tree verticality; trees with the same dimensions can differ substantially in value due to lean. The objective of this study was to assess the impact of tree leaning on estimation of stem dimensions using high-density terrestrial mobile lidar point clouds. We estimated lean with two metrics: the horizontal distance between stem centers at 1.3 m and 18 m, and the mean of seven successive lean angles along the tree bole (at 1, 3, 5, 7, 10, 12, and 15 m). For modeling, we used four existing taper equations and three existing volume equations. For trees leaning >2°, we enhanced the existing volume models by including lean as a predictor. Because lean estimates depend on the distribution and number of points describing the stem, we found that including the distance from scanner to tree improved the computed volume. When diameter at breast height was replaced with diameter at heights between 7 and 10 m, the volume models for leaning trees improved significantly, whereas the vertical trees had favorable results with heights between 5 and 15 m. Our study suggests that lean magnitude improves the estimation of stem volume when lean is >2°.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".