Development and annotation of perennial Triticeae ESTs and SSR markers
Bibliographic record
Abstract
Triticeae contains hundreds of species of both annual and perennial types. Although substantial genomic tools are available for annual Triticeae cereals such as wheat and barley, the perennial Triticeae lack sufficient genomic resources for genetic mapping or diversity research. To increase the amount of sequence information available in the perennial Triticeae, three expressed sequence tag (EST) libraries were developed and annotated for Pseudoroegneria spicata, a mixture of both Elymus wawawaiensis and E. lanceolatus, and a Leymus cinereus x L. triticoides interspecific hybrid. The ESTs were combined into unigene sets of 8 780 unigenes for P. spicata, 11 281 unigenes for Leymus, and 7 212 unigenes for Elymus. Unigenes were annotated based on putative orthology to genes from rice, wheat, barley, other Poaceae, Arabidopsis, and the non-redundant database of the NCBI. Simple sequence repeat (SSR) markers were developed, tested for amplification and polymorphism, and aligned to the rice genome. Leymus EST markers homologous to rice chromosome 2 genes were syntenous on Leymus homeologous groups 6a and 6b (previously 1b), demonstrating promise for in silico comparative mapping. All ESTs and SSR markers are available on an EST information management and annotation database (http://titan.biotec.uiuc.edu/triticeae/).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".