Multisite genetic parameter estimates from a <i>Callitropsis nootkatensis</i> diallel study with clonally replicated progeny
Bibliographic record
Abstract
Clonally replicated Callitropsis nootkatensis (D. Don) D.P. Little progeny from partial diallels were established in nine trials on coastal British Columbia, Canada. The trials were assessed for height, diameter, and crown form at age 12 years. An individual-genotype, linear mixed model with spatially correlated residuals was used to estimate the variance components and related genetic parameters. The majority of the estimated genetic variance for all traits was additive, and nonadditive genetic variance was predominantly due to dominance effects. Narrow-sense heritabilities for height and diameter at individual sites varied from 0.07 to 0.39, whereas for crown form, they were all less than 0.1. Dominance and epistasis ratios were, for the most part, lower than narrow-sense heritabilities. Common across-site additive and nonadditive genetic correlations were strongly positive and not significantly different from 1.0 for the majority of traits across sites within a series. Significant levels of additive genetic variance, coupled with insignificant to low nonadditive genetic variance for growth and crown form, would seem to be contrary to developing a clonal testing and deployment program. However, the lack of viable orchard seed and the faster delivery of genetic gain to reforestation, as well as more accurate forward selections based on additive genetic effects, makes this strategy viable for C. nootkatensis.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".