Simulation of hybrid forest tree breeding strategies
Bibliographic record
Abstract
Computer simulation is the only realistic method of evaluating alternative methods of breeding hybrid forest trees. Empirical tests would be very long term and expensive. This paper describes the development of a simulation program, called XSIM, which generates two different but closely related outcrossing tree species. The genetic correlation between performance in each parental species and performance in the resulting hybrid can be set, in addition to the amounts and types of variances in each parental species. The breeding strategies available for testing include conventional reciprocal recurrent selection, reciprocal recurrent selection with forward selection, recurrent selection within each pure species, and the creation of a synthetic species. XSIM allows the strategies to be compared using the same base populations, equivalent selection intensities, and comparable mating patterns. Innovative best linear unbiased prediction procedures allow all ancestral and current progeny generation data, from both parental species and the hybrid, to be analysed together. The theoretical basis for the simulation is given, and genetic and statistical models are described. In summary, XSIM allows rigorous comparisons of the strategies in terms of genetic gain per time and provides useful insight into hybrid forest tree breeding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".