High-throughput targeted SSR marker development in peach (<i>Prunus persica</i>)
Bibliographic record
Abstract
Simple sequence repeats (SSRs) have proven to be highly polymorphic, easily reproducible, codominant markers. However, developing an SSR map is very time consuming and expensive, and most SSRs are not specifically linked to gene loci of immediate interest. The ideal situation would be to combine a high-throughput, relatively inexpensive mapping technique with rapid identification of SSR loci in mapped regions of interest. For this reason, we coupled the high-throughput technique of AFLP mapping with subsequent direct targeting of SSRs identified in AFLP-marked regions of interest. This approach relied on the availability of peach bacterial artificial chromosome (BAC) library resources. We present examples of using this strategy to rapidly identify SSR loci tightly linked to two important, simply inherited traits in peach (Prunus persica (L.) Batsch): root-knot nematode resistance and control of the evergrowing trait. SSRs developed in this study were also tested for their transportability in other Prunus species and in apricots.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".