Bibliographic record
Abstract
Models taking genetic gain, relatedness, delay at generation turnover, and breeding population size into account have been developed to optimize selection age and generation interval. Relatedness (expressed as group coancestry) and average breeding value for the breeding population are merged into a joint index ("group merit"). The negative impact of group coancestry (like potential inbreeding depression) is expressed in a scale compatible with breeding value. Group merit measures the desirable characteristic of a breeding population. Annual increase of group merit is the criterion for comparing alternatives. Optimum generation interval is when annual group merit increase is highest. Generally the optimum selection age becomes higher when increase in relatedness is considered. We quantify the influence of relatedness penalty, early-mature genetic correlation, breeding population size, and delay at generation turnover on optimum selection age. A reasonable large population counteracts the increase of relatedness and, thus, favors early selection. Early selection can have a negative impact if a small early selection gain does not compensate for the buildup of relatedness at generation turnover. Conditions for this to occur are quantified. Early selection requires sufficient high juvenile-mature correlation to have a positive effect; this requirement can be reduced by using a large breeding population. The methods developed were applied to a number of situations relevant to forest tree improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".