Assessing structural differences among genetically improved coastal Douglas-fir using high density airborne laser scanning
Bibliographic record
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. Trials in British Columbia (BC), Canada aim to increase stem volume without sacrificing wood quality. Progeny test trials are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of trees. To evaluate performance of improved stock, and to determine whether yield gains are being met, realized-gain trials are used. In both trials, accurate and timely collection of phenotypic data are critical for estimating and validating BVs with confidence. Currently, phenotypic variables collected focus on yield attributes; namely diameter at breast height, and height. Selection criteria in BC are evolving; branching traits are recognized as having a strong influence on strength and stiffness of Douglas-fir wood, however, they are rarely measured. High-density Airborne Laser Scanning (ALS), as well as Remotely Piloted Aerial Systems Laser Scanning Systems (RPAS-LS) produce three-dimensional point clouds which can be used to characterize individual tree structure. In this dissertation I utilized metrics derived from ALS and RPAS-LS to assess the performance in realized-gain trials, and predict genetic parameters in progeny trials. Additionally, new methods to estimate branch attributes of individual trees for inclusion as selection criteria in tree improvement programs were developed. This dissertation provides an insight into how ALS can be used to model branch attributes, while the ability to analyse trees by plot, individual tree, and individual branch attributes further allows researchers to maximise the value of ALS data. The findings are encouraging; they indicate that branch level metrics can be included as selection criteria in breeding programs, and that ALS-derived BVs are a suitable proxy for ground-based BVs. Given the cost efficiency of ALS, forest geneticists should explore this technology as tool to increase breeding programs’ overall efficiency. Findings from this research can be integrated into large-scale programs for monitoring trees, and identifying trees that display desirable attributes.
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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 | 0.000 |
| 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) | 0.001 | 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".