Microsatellite-based characterization of the <i>Castanea sativa</i> cultivar heritage of southern Switzerland
Bibliographic record
Abstract
Southern Switzerland has a long tradition of chestnut cultivation as a staple food. Local inhabitants constantly selected varieties according to the ripening period, the type of use, and the adaptability to the territory. As a result, the panorama of chestnut varieties is very complex, as reflected by more than 120 different variety names in an area of 26,000 ha. Since 1994, 47 varieties have been conserved in the chestnut germplasm of southern Switzerland (CSS), including Marroni, Euro-Japanese, and French varieties. A selection of 164 individuals from the CSS was analysed by 8 SSR markers (4 of which were developed in this study). Microsatellite analysis indicated that the CSS was accurately established, as 86% of the individuals grafted were correctly labeled. The identification of 98 genotypes, 10 clonal chestnut groups, 4 synonym groups, and 12 homonym groups reflected the complex ethnogeographical structure of the chestnut distribution. The 17 Marroni individuals considered clustered in 2 differentiated genetic groups instead of only 1 as expected. The fundamental problem of the frequent cases of homonymy and synonymy is discussed, as is the need for criteria for discriminating between polyclonal varieties and distinct homonymous varieties.
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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".