Differentiation of triticale cultivars through FISH karyotyping of their rye chromosomes
Bibliographic record
Abstract
The aim of this work was to cytogenetically characterize triticale cultivars through fluorescence in situ hybridization (FISH) analysis of their rye chromosomes. In the present work, we studied six cultivars of triticale ('Cayú-UNRC', 'Cumé-UNRC', 'Genú-UNRC', 'Ñinca-UNRC', 'Quiñé-UNRC', and 'Tizné-UNRC'), released by the Universidad Nacional de Río Cuarto (UNRC), Córdoba, Argentina. The cultivars were obtained from the International Center for the Improvement of Maize and Wheat (CIMMYT) and improved for fresh forage, haymaking, and feed grain at UNRC. The distribution and organization of highly repetitive DNA sequences of Secale cereale (pSc74, pSc200, pSc250, and pSc119.2) using FISH analyses revealed a specific localization of the signals for several rye chromosomes, which allowed us to distinguish the cultivars. Cluster analysis showed a great cytogenetic similarity among the rye cultivars used to originate these hybrids. The knowledge of the variability among triticale cultivars is necessary to propose future crosses in breeding programs. This study will also be valuable to identify commercial seeds and to analyze the possible association between agronomic characters and the presence of certain rye chromosomes or specific regions in these chromosomes.
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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 | 0.000 |
| 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 | 0.001 |
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".