Genetic variation in direct and indirect measures of wood stiffness in coastal Douglas-fir
Bibliographic record
Abstract
We studied wood stiffness (estimated by modulus of elasticity, MOE), wood density, wood moisture content, and growth in a progeny test (50–130 families per trait; 1–3 sites) of coastal Douglas-fir ( Pseudotsuga menziesii var. menziesii (Mirb.) Franco). We measured MOE directly using lumber bending tests (MOEbl) and indirectly using tools (HM200 and ST300) that can be used to measure acoustic velocity in logs (VelHM) or standing trees (VelST). Acoustic MOEs in logs and standing trees (MOEHM and MOEST) were obtained from the velocities and green wood density. For backward selection, we estimated genetic gains in MOEbl of 8.6%–12.3%. Relative efficiencies (REs), the relative gains in MOEbl expected from indirect selection for correlated traits, were 78%–93% for the HM200 traits, 57%–58% for the ST300 traits, 38% for the basic wood density of basal discs (Denbd), and 98% for the oven-dry density of logs estimated from the lumber (Denol). The HM200 is an efficient tool for improving MOEbl, but gains will be lower using the ST300 on standing trees. Indirect selection on Denbd should be used with caution because the RE was low and Denbd was negatively correlated with growth (–0.49 to –0.73).
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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 | 0.000 |
| 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".