Simulation of the comparative gains from four different hybrid tree breeding strategies
Bibliographic record
Abstract
There is increasing interest in the deployment of interspecific hybrids in forest tree planting. The associated breeding programs are usually an adaptation of the reciprocal recurrent selection (RRS) strategy outlined by Comstock et al. (R. Comstock, H. Robonson, and P. Harvey. 1949. Agron. J. 41: 360367) or use recurrent selection for general combining ability (GCA) in the pure species. This study uses a computer simulation tool known as XSIM, which has been described in a previous paper, to investigate the efficiency of four hybrid strategies. In addition to conventional RRS, we considered RRS with forward selection (RRS-SF), a strategy that approximately halves the generation interval needed for RRS, because hybrid and pure species progeny are bred simultaneously. Forward and backward selections are also made simultaneously and not in successive generations as is the case for RRS. An innovative best linear unbiased prediction analysis makes this possible. The development of a synthetic species (SYN) and pure species selection (PSS) were other strategies tested. The strategies were tested across a wide range of genetic structures. Genetic structures were defined as particular combinations of the correlation between pure species and hybrid performance for each species and the proportion of the genetic variance that is additive, dominance, and epistatic for each species. The results of the simulation have shown that the SYN strategy is the most cost effective across a wider range of genetic structures. This is especially so for those structures where there is less dominance variance and the purehybrid correlations in both species are greater than zero. Where the SYN strategy is not cost effective, the RRS-SF strategy is then the best option.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".