Effects of early respacing on physico-mechanical properties of naturally regenerated <i>Picea sitchensis</i> in Great Britain
Bibliographic record
Abstract
Natural regeneration can reduce costs compared with replanting. However, its use requires knowledge about how either active or passive management will affect the balance between quality and quantity in the timber supply. This study aimed to quantify the effects of respacing on volume recovery and wood properties. Two British forest experiments using Picea sitchensis with various respacing distances and an un-respaced control were assessed 21–22 years after the treatments were applied. Tree dimensions were measured and used to quantify slenderness, merchantable volume, and sawlog volume. Wood properties were assessed on a sub-sample using mechanical testing. Generalised linear mixed models were used to examine differences between treatments and sites. Respacing decreased slenderness and increased relative sawlog volume and branch size. Wider respacing reduced wood strength and the widest respacing reduced wood stiffness. Respacing did not affect wood density. However, at the relatively low productivity sites considered here, respacing to 2.1 m represented the best compromise for current markets. In summary, not respacing improved some wood properties but reduced tree stability and the proportion and volume of sawlogs, which will negatively affect forest value.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".