Next-Gen sequencing of the transcriptome of triticale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Triticale possesses favourable agronomic attributes originating from both its wheat and rye progenitors, including high grain and biomass yields. Triticale, primarily used as animal feed in North America, is an excellent candidate for production of industrial bio-products. Little is known about the coordination of gene expression of rye and wheat genomes in this intergeneric hybrid, but significant DNA losses from the parental genomes have been reported. To clarify the regulation of gene expression in triticale, we carried out 454 sequencing of cDNAs obtained from root, leaf, stem and floral tissues in different lines of triticale and rye exhibiting different phenotypes and assembled reads into contigs. Related to the data assembly were the absence of reference genomes and the paucity of rye sequences in GenBank or other public databases. Consequently, we have sequenced cDNA libraries from roots, seedlings, leaves, floral tissues and immature seeds to facilitate the identification of triticale sequences originating from rye. To further characterize the wheat-derived cDNAs, we also developed a database close to 25,000 non-redundant full-length wheat coding sequence genes, based on existing databases and contigs that were verified against protein sequences from the grass genomes of Brachypodium distachyon , rice, sorghum and maize.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it