Effects of early respacing on the density and microfibril angle of Sitka spruce wood
Bibliographic record
Abstract
Although significant advances have been made in modelling the effects of silviculture on wood properties, few models have been calibrated using data from long-term stand density or respacing experiments. In this study we examined the effects of early respacing on the density and microfibril angle (MfA) of Sitka spruce (Picea sitchensis [Bong.] Carr.) wood using samples taken from a fully-replicated 57-year-old trial located in Northern Ireland, which had been thinned at age 11 years. Using a mixed-effects modelling approach, radial profiles of density and MfA from four different respacing treatments (1.83 m × 3.66 m, 3.66 m × 3.66 m, 3.66 m × 5.49 m and 5.49 m × 5.49 m) were compared with those of timber from an unthinned control (1.83 m × 1.83 m). After accounting for radial position and ring width, we found significant differences in both density and MfA between respacing treatments. Mean predicted values of wood density for rings 40–50 were 400 and 494 kg m−3 for the widest respacing treatment and the unthinned control, respectively, and fell between these two extremes for the other respacing treatments. Predicted latewood proportions in ring 50 were 12 and 22 per cent, for the same respacing treatments, respectively. There was some evidence of an age-related decline in wood density in the two narrowest respacing treatments. While there was a significant effect of respacing on MfA variation, the trends between respacing treatments were less apparent. Overall, these results indicate that the timing of respacing treatments is an important consideration in Sitka spruce management; early and severe respacing should be avoided to avoid deleterious effects on wood density and MfA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".