Genetic control of wood properties in Picea glauca — an analysis of trends with cambial age
Bibliographic record
Abstract
We investigated the genetic control of wood properties as a function of cambial age to enable improvement of juvenile wood attributes in white spruce ( Picea glauca (Moench) Voss). Increment cores were taken from 375 trees randomly selected from 25 open-pollinated families in a provenance–progeny trial repeated on three sites. High-resolution pith-to-bark profiles were obtained for microfibril angle (MFA), modulus of elasticity (MOE), wood density, tracheid diameter and cell wall thickness, fibre coarseness, and specific fibre surface with the SilviScan technology. Heritability estimates indicated that genetic control of cell anatomy traits and wood density increased with cambial age, whereas the genetic control of MFA and MOE remained relatively low across growth rings. Wood density, radial cell diameter, cell wall thickness, and specific fibre surface were highly heritable, indicating that significant genetic gains could be expected in tree improvement programs, although cambial age at selection may strongly influence the magnitude of realized gains. In contrast, growth-related properties, such as ring width, core length, and tree height, gave weak or nonsignificant heritability estimates. Adverse correlations between mechanical strength and properties related to paper quality suggest that breeding strategies must incorporate both types of traits to improve white spruce wood quality for different end uses.
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 | 0.000 |
| 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".