Evaluation of forest tree breeding strategies based on partial pedigree reconstruction through simulations: <i>Pinus pinaster</i> and <i>Eucalyptus nitens</i> as case studies
Bibliographic record
Abstract
Despite recent developments in molecular markers, most forest tree breeding programmes do not use them routinely. One way to integrate markers would be to use them for pedigree reconstruction after a simplified mating design through polymix or open-pollinated breeding. Thanks to the latest developments in the POPSIM simulator, various breeding strategies, including some based on paternity recovery, were evaluated with specified constraints on the level of diversity over breeding cycles. These simulations were carried out in two case studies: the French Pinus pinaster (Ait.) and the New Zealand Eucalyptus nitens (H. Dean & Maiden) Maiden breeding programmes. The Pinus pinaster case study produced lower genetic gain for the polymix breeding strategy with paternity recovery compared with double-pair mating or optimal-contribution strategies. However, the polymix breeding strategy could be of interest if the mating design is faster to complete. In the Eucalyptus nitens case study, pedigree recovery was shown to be a mandatory step to controlling the erosion of diversity over breeding cycles. In both cases, the strategies based on pedigree reconstruction were applicable with a limited level of genotyping. Finally, these simulations allow some general recommendations to be drawn to help breeders when designing a strategy for 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".