Variation of paternal contribution in a seed orchard of <i>Cryptomeria japonica</i> determined using microsatellite markers
Bibliographic record
Abstract
We investigated gene flow and pollen contamination in a seed orchard of Cryptomeria japonica D. Don using six microsatellite markers. The quality of a seed crop is determined by many factors, including the rate of pollen contamination from outside sources, degree of self-fertilization, and equality of the clones as pollen donors. The surveyed seed orchard consisted of 62 clones and a total of 360 ramets. The seeds from 12 mother trees in the seed orchard were investigated using six highly polymorphic loci with high multipaternity exclusion probability (0.999). The estimated average pollen contamination rate was high at 47.78% (±4.12%), and the clonal self-fertilization rate was 2.22% (±0.94%) when we assumed that null alleles were present. The high pollen contamination rate could dramatically reduce the quality of the seed crop. The contamination rate differed from tree to tree, but the differences were not related to the location of each individual. The levels of contribution as pollen donors differed significantly amongst clones in the seed orchard. Differences in flowering phenology and relative pollen fecundity may be responsible for the differences in paternal contributions. The effective distance of pollen dispersal in this C. japonica seed orchard seemed to be greater than previously estimated.
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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 | 0.000 | 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) | 0.000 | 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".