Apple SSRs present in coding and noncoding regions of expressed sequence tags show differences in transferability to other fruit species in Rosaceae
Bibliographic record
Abstract
Zhou, Y., Li, J., Korban, S. S. and Han, Y. 2013. Apple SSRs present in coding and noncoding regions of expressed sequence tags show differences in transferability to other fruit species in Rosaceae. Can. J. Plant Sci. 93: 183–190. Simple sequence repeat markers derived from expressed sequence tags (ESTs) are referred to as eSSRs. To develop molecular markers for non-model plants in Rosaceae, we investigated the transferability of apple eSSRs across seven fruit trees, belonging to four genera and 11 species of the Rosaceae family, including peach, quince, pear, loquat, apricot, cherry, and plum. Of the 98 apple eSSRs tested, 86 successfully amplified PCR products in at least one of the fruit tree species. Five apple eSSRs produced amplicons in more than five fruit tree species, and were deemed as a widely transferable Rosaceae marker set. Frequency of transferability of apple eSSRs across all seven fruit trees of Rosaceae varied widely among genera and species, with an average transferability of 29.0%. Overall, apple eSSRs transferred more easily to peach and pear than to plum and loquat. Interestingly, apple eSSRs present in coding sequences (CDS) showed higher levels of transferability to other fruit trees than those present in noncoding or untranslated regions (UTRs). Interestingly, apple eSSRs present in 5'UTRs showed lower frequencies of transfer than those present in 3'UTRs. The latter finding suggested that 5'UTRs might have diverged more rapidly than 3'UTRs in Rosaceae.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".