An enhanced molecular marker based genetic map of perennial ryegrass (<i>Lolium perenne</i>) reveals comparative relationships with other Poaceae genomes
Bibliographic record
Abstract
A molecular-marker linkage map has been constructed for perennial ryegrass (Lolium perenne L.) using a one-way pseudo-testcross population based on the mating of a multiple heterozygous individual with a doubled haploid genotype. RFLP, AFLP, isoenzyme, and EST data from four collaborating laboratories within the International Lolium Genome Initiative were combined to produce an integrated genetic map containing 240 loci covering 811 cM on seven linkage groups. The map contained 124 codominant markers, of which 109 were heterologous anchor RFLP probes from wheat, barley, oat, and rice, allowing comparative relationships between perennial ryegrass and other Poaceae species to be inferred. The genetic maps of perennial ryegrass and the Triticeae cereals are highly conserved in terms of synteny and colinearity. This observation was supported by the general agreement of the syntenic relationships between perennial ryegrass, oat, and rice and those between the Triticeae and these species. A lower level of synteny and colinearity was observed between perennial ryegrass and oat compared with the Triticeae, despite the closer taxonomic affinity between these species. It is proposed that the linkage groups of perennial ryegrass be numbered in accordance with these syntenic relationships, to correspond to the homoeologous groups of the Triticeae cereals.
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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 | 0.000 | 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) | 0.001 | 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 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".