Expressed sequence tag analysis in tef (<i>Eragrostis tef</i>(Zucc) Trotter)
Bibliographic record
Abstract
Tef (Eragrostis tef (Zucc.) Trotter) is the most important cereal crop in Ethiopia; however, there is very little DNA sequence information available for this species. Expressed sequence tags (ESTs) were generated from 4 cDNA libraries: seedling leaf, seedling root, and inflorescence of E. tef and seedling leaf of Eragrostis pilosa, a wild relative of E. tef. Clustering of 3603 sequences produced 530 clusters and 1890 singletons, resulting in 2420 tef unigenes. Approximately 3/4 of tef unigenes matched protein or nucleotide sequences in public databases. Annotation of unigenes associated 68% of the putative tef genes with gene ontology categories. Identification of the translated unigenes for conserved protein domains revealed 389 protein family domains (Pfam), the most frequent of which was protein kinase. A total of 170 ESTs containing simple sequence repeats (EST-SSRs) were identified and 80 EST-SSR markers were developed. In addition, 19 single-nucleotide polymorphism (SNP) and (or) insertion-deletion (indel) and 34 intron fragment length polymorphism (IFLP) markers were developed. The EST database and molecular markers generated in this study will be valuable resources for further tef genetic research.
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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 | 0.001 | 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) | 0.000 | 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".