Competition improves quality-related external stem characteristics of <i>Fagus sylvatica</i>
Bibliographic record
Abstract
Accurate information on the timber quality of hardwoods is often lacking, in particular for standing trees. In situ measurements of timber quality have the potential to improve the economic yield of a stand and may contribute to the optimal timing of a harvest and, in general, to improving forest management. Here, we used terrestrial laser scanning (TLS) to assess external timber quality metrics nondestructively. We investigated how competition intensity affected the metrics of 118 European beech (Fagus sylvatica L.) trees. We found that two newly developed TLS-based measures of external stem characteristics (number of bark anomalies per metre and stem non-circularity) were affected by competition intensity, suggesting that regulating competition levels may improve timber quality. Our study confirms empirical findings indicating a positive relationship between competition intensity and timber quality of European beech and offers a new methodology to assess external timber quality measures in the field objectively and nondestructively.
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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".