The use of ISSR markers to identify Texas bluegrass interspecific hybrids
Bibliographic record
Abstract
Abstract Seventeen ISSR primers were screened on Texas bluegrass (Poa arachnifera), Kentucky bluegrass (P. pratensis), Canadian bluegrass (P. compressa), Argentine bluegrass (P. ligularis), cv. ‘Sherman’ (P. secunda), putative Texas × Kentucky (TK) (P. arachnifera ×P. pratensis) hybrids and hybrids involving Texas, Canadian, and Sherman bluegrass [(P. arachnifera × P. compressa) × (P. arachnifera ×P. secunda)], to determine whether they could be used to produce robust and reproducible DNA fingerprints and identify interspecific hybrids. Nine of the 17 primers consistently produced robust fingerprints and nine 2‐way primer combinations were also selected. DNA fingerprints were highly reproducible and the majority of the selected primers (16/18) amplified hybrid profiles using two putative TK full‐sib hybrids. Combined with a rapid DNA extraction protocol, the ISSR technique enabled a fast and practical way to detect F1 interspecific hybrids early in the breeding programme and could also be useful for other applications that require DNA‐based markers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".