SNP-based markers for discriminating olive (<i>Olea europaea</i> L.) cultivars
Bibliographic record
Abstract
A set of 11 polymorphic markers (1 cleaved amplified polymorphic sequence (CAPS), 2 sequence-characterized amplified regions (SCARs), and 8 single-nucleotide polymorphism (SNP)-derived markers) was obtained for olive cultivar identification by comparing DNA sequences from different accessions. Marker development was more efficient, using sequences from the database rather than cloning arbitrary DNA fragments. Analyses of the sequences of 3 genes from 11 diverse cultivars revealed an SNP frequency of 1 per 190 base pairs in exons and 1 per 149 base pairs in introns. Most mutations were silent or had little perceptible effect on the polypeptide encoded. The higher incidence of transversions (55%) suggests that methylation is not the major driving force for DNA base changes. Evidence of linkage disequilibrium in 2 pairs of markers has been detected. The set of predominantly SNP-based markers was used to genotype 65 olive samples obtained from Europe and Australia, and was able clearly to discriminate 77% of the cultivars. Samples, putatively of the same cultivar but derived from different sources, were revealed as identical, demonstrating the utility of these markers as tools for resolving nomenclature issues. Genotyping data were used for constructing a dendrogram by UPGMA cluster analysis using the simple matching similarity coefficient. Relationships between cultivars are discussed in relation to the route of olive's spread.
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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 | 0.001 | 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) | 0.000 | 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".