Crown structure of European beech (<i>Fagus sylvatica</i>): a noncausal proxy for mechanical–physical wood properties
Bibliographic record
Abstract
The current tendency towards the silvicultural promotion of mixed tree species has increased the variability in the crown structure within stands. This study shows how neighbouring trees can influence both the external crown features and internal wood properties of trees. Using terrestrial laser scanning, the crown features of 100 European beech trees (Fagus sylvatica L.) from pure beech stands and mixed stands of beech with Douglas fir (Pseudotsuga menziesii (Mirb.) Franco), Norway spruce (Picea abies (L.) Karst.), sessile oak (Quercus petraea (Matt.) Liebl.), and Scots pine (Pinus sylvestris L.) were recorded. After felling and sawing, the dynamic modulus of elasticity was determined on 1623 boards from the two lowest 4.1 m logs. Significant differences were found between beech trees from pure stands and those from beech–pine mixed stands in terms of crown volume (415 vs 766 m3), crown ratio (50.0% vs 71.5%), crown projection ratio (0.182 vs 0.253 m·cm−1), and branch angle (30.7° vs 54.1°). Multiple regression mixed models revealed significant relationships between timber stiffness and crown volume (–1.7 N·mm−2·m−3), crown ratio (–28.4 N·mm−2·%−1), and crown projection ratio (–9835 N·mm−2·m−1·cm). Thus, the crown morphology of broad-leaved species reflects the tree’s long-term competitive status and suggests indicators for the assessment of mechanical–physical wood properties.
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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".