Effect of growth rate on the physical and mechanical properties of Douglas-fir in western Europe
Bibliographic record
Abstract
To quantify the impact of forest management practices and tree growth rate on the potential uses of Douglas-fir wood, nine physico-mechanical properties were studied on more than 1250 standardized clear specimens. These were collected from trees cut in 11 even-aged stands (six trees per stand) located in Wallonia (southern Belgium). Stands were 40 to 69 years old, and mean tree girth was ca. 150 cm. Mean ring width of the 66 trees ranged from 3 to more than 7 mm. Statistical analysis showed significant but weak effects of ring width on the studied properties. Considered jointly, mean ring width and cambial age of the test specimens only explained 28% to 40% of the variability of their properties. Also, when ring width increases, these properties display higher decreases in juvenile wood than in mature wood. From a technological standpoint, maintaining mean ring width under 4 mm in juvenile wood and 6 mm in mature wood should accommodate all potential uses of Douglas-fir wood. However, considering that density appeared to be the main driver of wood properties, our results and the literature corroborate the importance of genetic selection as a complement to silvicultural measures to improve or guarantee the technological properties of Douglas-fir wood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".