Genetic parameter estimates for wood stiffness, strength, internal checking, and resin bleeding for radiata pine
Bibliographic record
Abstract
Data from two separate experiments were used to estimate genetic parameters for different wood properties of Pinus radiata D. Don in New Zealand. In experiment 1, 72 and 60 open-pollinated families were sampled at two sites, Kinleith and Paengaroa, respectively. Fifty-nine such families were common to both sites. In experiment 2, 62 and 29 pollen-parent families were sampled from a female-tester trial at two sites, Kaingaroa and Warrengong (the latter in Australia), respectively. Twenty-nine such families were common to both sites. Clearwood stiffness (MOE) and strength (MOR), wood density (DEN), acoustic stiffness on standing trees (IMLH), acoustic stiffness in 5-m logs (HITMAN), external resin bleeding (ERB), internal checking from discs taken at 1.3-m and 3.0-m height (CHEK_B and CHEK_U), and diameter at breast height (DBH) were assessed. Within-site narrow-sense heritability (h2) estimates of various wood properties were consistent, particularly within New Zealand. Across-sites estimates of h2 of HITMAN, CHEK_B, and DEN were, respectively, 0.24, 0.40, and 0.62 in experiment 1 and 0.21, 0.16, and 0.38 in experiment 2. Estimated type B genetic correlations were generally high for all traits except DBH. DEN showed strong positive genetic correlation with all direct and acoustic measures of stiffness (MOE, HITMAN, and IMLH). DEN and all measures of stiffness showed low to moderate negative genetic correlations with DBH.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".