Can wood properties be predicted from the morphological traits of a tree? A canonical correlation study of plantation-grown white spruce
Bibliographic record
Abstract
There is a growing interest in predicting wood quality from tree morphology traits, which can be measured using remote sensing techniques such as LiDAR, to enhance forest inventory for operational planning. In this study, we investigated the correlation structure between these two categories of traits in white spruce (Picea glauca (Moench) Voss) using canonical and multiple regression analyses with the objective of identifying key morphology variables that are predictive of wood quality. For 495 trees from a 30-year-old plantation, we obtained measurements of tree height and dimensions of the living crown, as well as the number and diameter of live branches at selected whorls. Wood traits were assessed from wood cores with SilviScan technology. Morphological traits explained almost 29% of the overall variation observed in wood traits. However, the magnitude of the correlations and the ability of crown morphological traits to predict wood traits differed widely among the latter. Average ring width and radial cell diameter, both related to increment, were well correlated with tree morphology, whereas traits related to subcellular structure, for instance, microfibril angle, were poorly correlated. These results could guide the choice of wood traits to improve inventory techniques aiming to optimize the forest product value chain.
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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 | 0.001 | 0.002 |
| 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".