Detection of new genetic profiles and allelic variants in improperly classified grapevine accessions
Bibliographic record
Abstract
Thirty-seven grapevine accessions, collected in Central Italy, were characterized by morphological and genetic analysis, according to guidelines developed by European Union programs of grapevine research and standardization. Traditional denominations of some sampled varieties were revealed to be incorrect; moreover, 10 synonymies and 12 homonymies were recognized. Ampelographic and ampelometric measurements of leaf characters were performed. These data generated a phenotypic similarity matrix and a relative diagram showing morphological differences between specimens. Many samples presented different morphology even in the presence of the same genotype, probably as a result of various environmental pressures. Grapevines were typed by 12 microsatellite loci and then compared with the CRA-VIT genetic resource database. Twenty-five SSR profiles were clearly identified as well-known cultivars, while nine genotypes did not find a direct correspondence: these samples could represent putative new autochthonous Latial Vitis vinifera cultivars or hybrid varieties. The genetic approach also detected three new (169 and 173 in VVMD27 locus; 179 in ISV2 locus) and seven rare allelic variants. Plant sample classification by oral history, morphological observations, and molecular results were compared and discussed. Scions of samples were planted in the Botanic Garden of the University of Rome "Tor Vergata", to preserve grapevine biodiversity and to protect possible new autochthonous 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 | 0.001 |
| 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".