Genetics of wood stiffness and its component traits in<i>Pinus radiata</i>
Bibliographic record
Abstract
The potential for breeding Pinus radiata D. Don to improve wood stiffness (modulus of elasticity, MoE) was examined by obtaining pith-to-bark cores from trees at breast height in two independent genetic trials. The effectiveness of early selection for stiffness and indirect selection on the component traits, microfibril angle (MfA) and wood density, was determined as well as the age-related changes in the genetic variation of these traits. The first trial comprised 50 open-pollinated families in the central North Island, New Zealand. The second trial comprised 20 control-pollinated families in New South Wales, Australia. The genetic control of MfA, density, and MoE was found to be high in the corewood and moderate in the outerwood. Estimated genetic correlations suggested that early selection for most traits would be successful but could be carried out slightly earlier at the New Zealand site than at the Australian site. To maximize gain in the corewood, selection for MoE and MfA would be most effective around rings 4-8. There were no adverse correlations between MoE and MfA or density, implying that selection for MoE would also improve MfA and density.
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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) | 0.001 | 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".