Genetic relationships analysis of olive cultivars grown in China
Bibliographic record
Abstract
The olive tree is an iconic tree of the Mediterranean, and is used extensively to produce high-quality olive oil. Although the China olive industry has just begun to be valued, there were also existed mislabeling and synonyms in introduced cultivars. The aim of this study was to analyze genetic similarities among olive cultivars in China using SSR and ISSR techniques. Thirty-two samples were collected from Xichang. Five of these cultivars were issued from a Chinese breeding program. Genomic DNA samples were extracted from young leaves and PCR was used to generate SSR and ISSR markers. A total of 107 polymorphic bands were detected on thirteen SSR loci, with an average of eight alleles per locus. The observed heterozygosity ranged from 0.785 (DCA03) to 0.990 (GAPU47), and the expected heterozygosity varied between 0.782 (DCA03) and 0.940 (GAPU103A). The discrimination power ranged from 0.57 to 0.83, while the polymorphism information content values ranged from 0.768 (DCA03) to 0.934 (GAPU103A). Nine ISSR primers generated 85 reproducible bands of which 78 (91.8%) were polymorphic. Based on our data, genetic similarity between cultivars ranged from 0.57 to 0.83. Cluster analysis revealed that 32 cultivars were clustered into six groups, which supports similar morphology such as use, oil content and fruit weight but not similar geographical origins. Our data also allow the identification of unknown cultivars and cases of synonyms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".