Genetic variation in wood properties of interior spruce. II. Tracheid characteristics
Bibliographic record
Abstract
In this study we investigated quantitative genetic variation in tracheid characteristics in two genetic tests of British Columbia's interior spruce (the common name for white spruce, Picea glauca (Moench) Voss; Engelmann spruce, Picea engelmanni Parry ex Engelm.; and their hybrids). The study included 88 half-sib families from the East Kootenay and Prince George regions. We have developed a technique for quantitative assessment of tracheid characteristics by measuring cross-sectional dimensions. We obtained cell size, wall thickness and their ratio in early-, transition-, and late-wood classes within a growth ring. Tracheid length and microfibril angle were measured in the transition wood. A number of tracheid characteristics showed significant genetic variation, but heritability, phenotypic, and genetic correlation estimates varied across test sites within and outside regions of origin of parental trees. Ring width was determined, both phenotypically and genetically, by the number of tracheids and to a lesser extent by the by their mean size. On average, rings with larger tracheids did not have significantly thicker walls. Wider rings had lower mean wall to tracheid size ratio. Faster growth did not result in shorter tracheids in the transition wood. Longer tracheids had lower micro fibril angle. There were no particular benefits from considering the anatomical component traits for breaking the negative genetic correlation between growth and wood density.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".