Selecting Dry Fiber Weight For Higher and Better Quality Jack Pine Fiber Production
Bibliographic record
Abstract
Sixteen-year-old half-sib jack pine (Pinus bankslana Lamb.) families planted in New Brunswick were evaluated for wood density, growth traits (DBH, tree height, and bole volume), and dry fiber weight (wood density x bole volume). The variation and inceritance of these traits and their relationships were studied. The implications of these genetic parameters for optimum gains in wood quality and wood quantity (bole volume) were discussed. The results indicate that wood density and tree height exhibit considerably less phenotypic variation but a remarkably higher heritability compared to DBH and bole volume. Dry fiber weight shows the largest phenotypic variation but a moderate heritability. There exists a positive genetic correlation between wood density and all growth traits. This suggests that selection for growth traits would not necessarily lead to a reduction in wood density in this species. Compared to traditional selection for bole volume, however, selection for dry fiber weight would result in higher genetic gains not only in dry fiber weight (+12.9% vs. 9.9%), but also in wood density (1.8% vs. 0.8%) and bole volume (9.8% vs. 8.2%). Therefore, this selection strategy would achieve both higher and better quality fiber production compared to traditional selection for volume alone.
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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 | 0.000 | 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".