The effects of initial spacing on wood density, fibre and pulp properties in jack pine (Pinus banksiana Lamb.)
Bibliographic record
Abstract
Abstract Relationships between basic tree and wood properties, and species, seed source, geographic location, site conditions and management decisions are very complex. The objective of this study was to quantify the effects of forest management practices on wood density, fibre and pulp properties in jack pine, one of the most important commercial species in Northern America. A better understanding of the relationship between initial spacing and wood and end-product quality should help define improved forest management strategies required to produce quality wood and products in the future. On the basis of the oldest jack pine initial spacing trial established in 1941 by the USDA Forest Service, this study examined the impact of four different initial spacing trials on tree growth, wood density, fibre and pulp properties of jack pine. The results clearly show that initial stand spacing has a significant effect on all of these properties, and thus it is possible to improve yield and wood and pulp fibre properties of jack pine through stand density regulation. Additionally, a positive effect of pre-commercial thinning on fibre properties was also demonstrated. As a consequence of these results, basic prescription information for decision-making in the establishment of jack pine plantations with desirable pulp properties can be elucidated.
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 | 0.000 | 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".