Associations of SRAP markers with dried-shrink disease resistance in a germplasm collection of sea buckthorn (<i>Hippophae</i> L.)
Bibliographic record
Abstract
Sea buckthorn (Hippophae L.) is a woody, outcrossing dioecious pioneer plant, being widely planted as a new berry crop with rich nutritional and medicinal compounds. This long-juvenile and long-lived woody plant can be more difficult to cultivate than other crop plants. Dried-shrink disease (DSD) is a dangerous pathogen that destroys sea buckthorn and halts commercial production. We estimated variability of sequence-related amplified polymorphism (SRAP) markers in 77 accessions of 22 sea buckthorn cultivars to seek markers associated with DSD resistance and help to identify potential breeding cultivars. Seventeen SRAP primer combinations generated 289 bands, with a mean of 17 bands per primer combination. At a Dice coefficient of 0.852, the dendrogram generated with 191 polymorphic bands clustered 73 accessions of Hippophae rhamnoides into 2 groups and 4 accessions of Hippophae salicifolia into 1 group. Eleven SRAP markers (Me1-Em3(600), Me1-Em1(680), Me2-Em1(650), Me2-Em1(950), Me3-Em6(1300), Me2-Em6(320), Me2-Em6(400), Me1-Em2(600), Me1-Em1(1200), Me1-Em1(1700), Me2-Em2(250)) were significantly correlated with DSD resistance (P < 0.001). These markers provide a viable option for breeding programs that select lineages with DSD resistance, especially when no other genetic information, such as linkage maps and quantitative trait loci, are available.
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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) | 0.001 | 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".