Lumber and wood chips properties of dead and sound black spruce trees grown in the boreal forest of Canada
Bibliographic record
Abstract
Little attention has been given to changes in wood properties after isolated mortality events, which characterize the gap dynamics of several forest ecosystems. For the forest industry, dead and sound trees may represent a significant source of timber supply, but of potentially lower quality. The main objective of this study was therefore to compare the properties of wood obtained from dead and sound wood (DSW) trees with those from live trees. In total, 162 black spruce trees (Picea mariana (Mill.) BSP) were felled from three sites comprising three states of tree degradation and three diameter classes. In total, 822 pieces of lumber of different dimensions were produced and visually graded. Full-size lumber pieces of 4.3–5.0 m in length (n = 343) were tested for wood stiffness and strength in longitudinal static bending. Samples of wood chips and bark were also collected during the production process at a sawmill. Results indicate that DSW trees produced lumber of significantly poorer mechanical properties than live trees. For the same modulus of elasticity (MOE) value, DSW trees have significantly lower modulus of rupture values than those of live trees, especially for MOE values of <15 GPa. This suggests that the wood of DSW trees is more brittle, a fact that should be taken into account for the production of machine-stress-rated lumber. Moisture content of wood chips was significantly lower in DSW trees, although it remained above the fibre saturation point. The amount of wood fibre attached to the bark was significantly higher in DSW trees. Considering these differences, DSW trees can be expected to provide wood of inferior quality than live trees but which still meet the technical requirements ( Barrett and Lau, 1994) for producing structural lumber.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".