Effect of Seed Transfer on Selected Wood Quality Attributes of Jack Pine (Pinus banksiana Lamb.)
Bibliographic record
Abstract
So far, few studies have considered the impacts of seed sources transfer on jack pine (Pinus banksiana Lamb.) wood quality, although wood quality attributes (WQA) in general and the differences between juvenile and mature wood in particular will determine suitability of the produced wood for end-uses. The main objective of this study was to examine the possibility of selecting superior jack pine provenances based on selected WQA. Twenty-two provenances of jack pine were planted in 1964 in Petawawa Research Forest, ON, Canada, as part of a provenance test. The plantation location offers conditions close to optimum for jack pine growth. Transition ages at breast height, determined with tracheids length, were computed with a piecewise model. Measurements at age 42 from seed were subjected to analyses of variance. Radial variations from pith to bark, as well as trends with seed sources origin of the selected WQA were also considered. A ranking was made based on a selection index built with four WQA. The provenances matured between 8 and 14 years, corresponding to 17%–48% of juvenile wood proportion. Significant differences among provenances were observed for ring width, ring density, tracheid length, and diameter at breast height but not for tracheid diameter, tree height, transition age, and juvenile wood proportion. None of the provenances ranked the best with all the selected WQA, but it was possible to find provenances exhibiting both high growth rate and good wood quality. A surprising result of this study was that tracheid diameter initially enlarged for 8 years, before declining toward the bark. It is possible to select provenances for a higher growth rate and for good physical (i.e., related to wood density) and anatomical (i.e., related to tracheid dimensions) wood quality attributes.
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 | 0.001 |
| 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".