Influence of selective breeding on the development of juvenile wood in Sitka spruce
Bibliographic record
Abstract
The effect of selective breeding on juvenile wood formation in 24-year-old Sitka spruce (Picea sitchensis (Bong.) Carr.) was investigated. Properties associated with juvenile wood in fast-growing progenies were compared with those from slow-growing progenies and an unimproved control of similar growth rate (origin Queen Charlotte Islands, British Columbia, Canada). Large differences in properties associated with juvenile wood, namely high annual ring width, high microfibril angle, low density, and low latewood proportion, were observed in the first 12 or so rings from the pith between treatments. These properties were significantly inferior in the fast-growing progenies in comparison with the slow-growing treatments. From the 13th ring outwards, no significant differences were found between treatments in all attributes measured. Coefficients of determination (R2) between ring width and wood properties measured from rings 1 to 12 revealed only weak associations. Conversely, R2 values calculated for rings 1319 revealed significant associations, indicating that density, latewood proportion, and tracheid length and diameter declined, while microfibril angle increased, with increasing ring width. The period of formation and the properties of juvenile wood appear to be largely independent of growth rate. High growth rate in the mature wood remains a concern in terms of wood quality.
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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 | 0.001 |
| 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) | 0.000 | 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 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".