Integrating fecundity variation and genetic relatedness in estimating the gene diversity of seed crops: <i>Pinus koraiensis</i> seed orchard as an example
Bibliographic record
Abstract
The genetic gain and gene diversity of seed crops from a 1.5-generation clonal seed orchard of Pinus koraiensis Siebold & Zucc. were estimated under consideration of parental genetic values and fecundity variation. Fecundity variation among clones was estimated for 5 consecutive years (2010–2014) as the sibling coefficient, which was drawn from clonal contribution to the total production of seed conelet. To monitor gene diversity, status number was estimated by the integration of fecundity variation and group coancestry. Group coancestry was calculated as the average of genetic relatedness (coancestry) among orchard clones. The averages of conelet production were high in 2010 and 2011, moderate in 2013 and 2014, and poor in 2012 with a grand mean of 13.7. Correlation analysis showed that good conelet producers consistently gave good production. Cumulative distribution of clonal conelet production was presented as a function of the total conelet yield, and this distribution indicated deviation from the expected clonal equal production. Group coancesrtry was 0.0096, indicating minimal loss of gene diversity. Status number and genetic gain were higher in good than in poor conelet production years, highlighting the importance of fecundity variation in determining the genetic gain and gene diversity of seed orchard crops.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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".