Age-related Trends in Genetic Parameters for Jack Pine and Their Implications for Early Selection
Bibliographic record
Abstract
Abstract Trends in genetic parameters for height growth of jack pine (Pinus banksiana Lamb.) were examined over three series of family tests throughout New Brunswick. Data were analyzed for each site and across sites within each series. Although individual narrow sense heritability estimates from single-site analyses varied substantially from site to site and showed no consistent age-related pattern, the estimates from across-site analyses showed an increasing trend to age 20. Similar as individual narrow sense heritability, the coefficient of additive genetic variance estimated from single site showed more variation than those estimated from across site analyses. Age-age (type-a) genetic correlations for height were high and could be well predicted by a LAR2model, where LAR is the natural logarithm of the ratio between two ages at assessment. Type-b genetic correlations were high and of similar magnitude at different ages. Genetic correlations between height at different ages and volume at one-half rotation age were generally high. Taking the volume at one-half rotation age as the target trait, the selection for target trait from early selection at ages 5~7 could be more efficient per year than direct selection.
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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".