Design strategy of advanced generation breeding population of Pinus tabuliformis based on genetic variation and inbreeding level
Bibliographic record
Abstract
The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement. The relationship between genetic variation level within Pinus tabuliformis breeding populations and selection strategies or selection effectiveness is not fully investigated. Here, we compared the selection effectiveness of combined and individual direct selection strategies using half- and full-sib families produced from advanced-generation P . tabuliformis seed orchard as our test populations. Our results revealed that, within half-sib families, average diameter at breast height (DBH), tree height, and volume growth of superior individuals selected by the direct selection strategy were higher by 7.72, 18.56, and 31.01%, respectively, than those selected by the combined selection strategy. Furthermore, significant differences ( P < 0.01) were observed between the two strategies in terms of the expected genetic gains for average tree height and volume. In contrast, within full-sib families, the differences in tree average DBH, height, and volume between the two selection strategies were relatively minor with increase of 0.17, 2.73, and 2.21%, respectively, and no significant differences were found in the average expected genetic gains for the studied traits. Half-sib families exhibited greater phenotypic and genetic variation, resulting in improved selection efficiency with the direct selection strategy but also introduced a level of inbreeding risk. Based on genetic distance estimates using molecular markers, our comparative seed orchard design analysis showed that the Improved Adaptive Genetic Programming Algorithm (IAPGA) reduced the average inbreeding coefficient by 14.36% and 14.73% compared to sequential and random designs, respectively. In conclusion, the combination of the direct selection strategy with IAPGA seed orchard design aimed at minimizing inbreeding offered an efficient approach for establishing advanced-generation P . tabuliformis seed orchards.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".