Genetic analysis of fiber-dimension traits and combined selection for simultaneous improvement of growth and stiffness in lodgepole pine (<i>Pinus contorta</i>)
Bibliographic record
Abstract
Quantitative genetic variation of fiber-dimension traits and their relationship with diameter at breast height (DBH) and solid-wood traits (i.e., density and modulus of elasticity (MOE)) was investigated in lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). A total of 823 increment cores were sampled from 207 half-sib families at two independent progeny trials, aged 34–35 years, located in northern Sweden. High-resolution pith-to-bark profiles were obtained for radial fiber width (RFW), tangential fiber width (TFW), fiber wall thickness (FWT), and fiber coarseness (FC) using SilviScan. Heritabilities ranged from 0.29 to 0.74, and inheritance increased with cambial maturity. Estimated age–age genetic correlations indicate that early selection between ages 5 and 8 years is highly efficient. Our results indicate that selection for a 1% increase in DBH or MOE incurs a negligible effect on fiber-dimension traits and maximum genetic gains are reached when DBH and MOE are considered jointly. Moreover, simultaneous improvement of growth and stiffness is achievable when a selection index with 7 to 10 economical weights for MOE relative to 1 for DBH is incorporated. However, the unfavorable relationship between solid-wood traits and pulp and paper related traits suggests that breeding strategies must be implemented to improve wood quality of lodgepole pine for multiple uses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".