Application of inter-simple sequence repeats relative to simple sequence repeats as a molecular marker system for indexing blueberry cultivars
Bibliographic record
Abstract
Garriga, M., Parra, P. A., Caligari, P. D. S., Retamales, J. B., Carrasco, B. A., Lobos, G. A. and García-Gonzáles, R. 2013. Application of inter-simple sequence repeats relative to simple sequence repeats as a molecular marker system for indexing blueberry cultivars. Can. J. Plant Sci. 93: 913–921. Chile, the major exporter of blueberries in South America, grows two species commercially, the highbush blueberry (Vaccinium corymbosum L.) and the rabbiteye blueberry (Vaccinium ashei Reade). Considering the increasing demands for this fruit, it is necessary to have reliable methods for genotyping and genetic traceability of the commercially grown cultivars. In this study, an inter-simple sequence repeat (ISSR) marker-based system was established to perform the genetic identification of these cultivars. Ten cultivars of V. corymbosum: ‘Bluecrop’, ‘Bluegold’, ‘Duke’, ‘Elliott’, ‘Legacy’, ‘Misty’, ‘Nelson’, ‘O'Neal’, ‘Sierra’ and ‘Toro’ and three of V. ashei: ‘Climax’, ‘Premier’ and ‘Tifblue’ were analyzed. The sensitivity and reliability of this molecular marker system was compared with identification by simple sequence repeats (SSR). Six ISSR primers were used and high levels of polymorphism among the cultivars (80% of polymorphic loci) were detected, with high repeatability. Using individual primers, distinguishing among cultivars was possible in three cases. However, using pairs of ISSR primers provided greater reliability in cultivar identification. This ISSR-based technology is a simpler, faster, and less expensive alternative to SSRs for genotyping blueberry cultivars and can be used in genetic traceability studies as well as genetic improvement programs.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".