Deriving internal crown geometric features of Douglas-fir from airborne laser scanning in a realized-gain trial
Bibliographic record
Abstract
Abstract Tree improvement programs are critical to establishing high yield seed sources while maintaining genetic diversity and developing sustainable plantation forests. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is commonly used in improvement programs due to its superior strength and stiffness properties. Progeny testing trials in British Columbia (BC), Canada aim to increase stem volume without sacrificing wood quality. As genetically improved stock in BC is entering its fourth generation, wood quality and branch attributes are becoming more important as selection criteria evolve. This study investigates the utility of Airborne Laser Scanning (ALS) to produce metrics that describe branch attributes, and test whether these attributes showed differences in trees of three different genetic gain levels (GL); Wild-Stand (WS), Mid-Gain (MG) and Top-Cross (TC), planted at two different spacings (2.9 m and 4.0 m) in a realized-gain trial. New methods were developed to utilize ALS data to estimate metrics such as branch angle, length and volume using a point clustering approach. The relative impact of GL and spacing on branch attributes were assessed. Spacing was significant for branch angle (2.9 m = 73.53°, 4.0 m = 72.46°), whereas GL (WS = 0.861 m, MG = 0.917 m, TC = 0.948 m) and spacing (2.9 m = 0.884, 4.0 m = 0.942) were significant for length. For all metrics, TC trees at 4.0 m spacing were consistently significantly different whenever GL or the interaction effect was significant. This data provide an insight into how ALS can be used to model branch attributes, whereas the ability to analyse trees by plot, individual tree and individual branch attributes further allows researchers and foresters to maximize the value of ALS data. Findings from this research can be integrated into large-scale programs not just for monitoring trees, but also for identifying new trees that can display attributes associated with larger volumes and increased value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".