Genetic influence on components of wood density variation in white spruce
Bibliographic record
Abstract
Abstract Conifer breeding programmes are increasingly selecting for wood quality in addition to growth so to ensure a sufficient flow of high-quality fibre from future forest plantations. As wood density is one of the most frequently used indicators of wood quality, there is a growing interest to consider this trait in selection, and thus enhance the properties of end-use products from planted trees. However, wood density varies at different scales within trees, with pith-to-bark patterns and year-to-year fluctuations representing two important sources of variation. From both physiological and end-use points of view, it is desirable to produce stems with limited pith-to-bark and year-to-year wood density variation. In the present study, we assessed patterns of pith-to-bark and year-to-year variation in 2196 wood density patterns and evaluated the genetic control of traits characterizing this variation. The experimental data came from a 15-year-old white spruce genetic trial representing 93 full-sib families replicated in two contrasting environments in Quebec, Canada. To separate pith-to-bark from year-to-year variation, non-linear models were developed to describe pith-to-bark patterns of variation in the mean ring density (MRD) of individual trees as well as for latewood density (LWD) and latewood proportion. We observed that pith-to-bark variation was more under genetic control than year-to-year variation, for which only LWD and proportion of latewood width to overall ring width reached moderate genetic control. Little genotype-by-environment interaction was observed although wood density patterns differed significantly between sites. The present approach could help identify trees or families that tend to have limited pith-to-bark and year-to-year variation in wood density as part of tree genetic improvement programmes to provide future trees with more uniform and desirable wood attributes.
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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 | 0.001 | 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 | 0.001 | 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".