Comparing gain and optimum test size from progeny testing and phenotypic selection in <i>Pinus sylvestris</i>
Bibliographic record
Abstract
The profit from tree breeding is dependent on the amount of money invested and how these resources are spent, particularly in the testing of selection candidates. Simulations of within-family selection were used to find the optimum balance among the number of candidates, progenies per candidate, and test sites for a given investment level and to compare the profit from progeny testing and phenotypic selection. The simulations were based on genetic parameters estimated from 66 Pinus sylvestris L. progeny trials in southern Sweden and on compilations of breeding costs. For progeny testing the optimum number of candidates and test sites increased with increasing investment level, whereas the number of progenies per candidate and site decreased and stabilized at ca. 10 individuals. The maximum annual profit for the phenotypic selection was higher and occurred at a lower investment level than for progeny testing. Among the two alternatives of progeny testing studied, the intensive alternative with practices to stimulate early flowering showed a higher maximum annual profit than the base alternative.
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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 | 0.000 | 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".