Genetic analysis of lodgepole pine (<i>Pinus contorta</i>) solid-wood quality traits
Bibliographic record
Abstract
Potential improvement of lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) solid-wood properties was examined by estimating age trends of inheritance, age–age genetic correlations, and the efficiency of early selection using 823 increment cores sampled from 207 half-sib families at two independent progeny trials, aged 34–35 years, located in northern Sweden. High-resolution radial variation of annual ring width, wood density, microfibril angle (MFA), and modulus of elasticity (clearwood stiffness; MOES) was measured using SilviScan. The dynamic stiffness (MOEtof) of standing trees was also obtained using Hitman ST300. Heritabilities ranged from 0.10 to 0.64 for growth and earlywood, transition-wood, and latewood proportions, from 0.29 to 0.77 for density traits, and from 0.13 to 0.33 for MFA and stiffness traits. Genetic correlations between early age and the reference age (26 years) suggested that early selection is efficient at age 4 years for MFA and between ages 5 to 8 years for density and MOES. Unfavorable diameter–stiffness genetic correlations and correlated responses indicate that breeding for a 1% increase in diameter would confer 5.5% and 2.3% decreases in lodgepole pine MOES and MOEtof, respectively. Index selection with appropriate economical weights for growth and wood stiffness is highly recommended for selective breeding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".