Characterization of Spanish grapevine cultivar diversity using sequence-tagged microsatellite site markers
Bibliographic record
Abstract
A broad germplasm bank collection containing most of the autochthonous Spanish grapevine cultivars was analyzed using six sequence-tagged microsatellite site (STMS) loci: VVS2, VVMD5, VVMD7, ssrVrZAG47, ssrVrZAG62, and ssrVrZAG79. The number of alleles obtained ranged from 9 in ssrVrZAG47 to 13 in VVS2, and the observed genotypes per locus varied between 24 (ssrVrZAG47) and 41 (VVSS2). A total of 57 unique genotypes were obtained considering all 6 loci, and 40 varieties presented at least 1 of these specific genotypes. The genotypic combinations for the 6 loci have generated 163 different profiles in the 176 cultivars. Ten pairs of accessions and one group of four Garnacha's cultivars can not be differentiated. The observed heterozygosity varied between 75.6 (VVMD7) and 90.9% (VVMD5), without significant differences from the expected values for any loci. The VVMD5 locus was the most informative, and also showed the highest discrimination power. The cumulative discrimination power for all six loci was practically 1; however, in fact, these STMS loci have differentiated only about 93% of the accessions, probably owing to high relatedness of the plant material. Usefulness of this STMS set for characterization of a Spanish grapevine collection is emphasized, as well as the elaboration of databases with these molecular markers.
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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 | 0.001 |
| 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".