87Sr/86Sr isotope ratios in soils, vine leaves, grapes and wines of the Italian volcanic districts authenticate their respective terroirs
Bibliographic record
Abstract
Interest in the origin and traceability of agri-food products has led to an increasing number of publications using strontium isotope ratios as a geographic tracer. We used 87 Sr/ 86 Sr isotope systematics to authenticate the provenance of wine from different volcanic districts of Italy. Samples of soil, grapes, leaves and bottled wines from 6 different wineries were analysed. A detailed study of the different soil horizons from the Somma-Vesuvio area demonstrates the relationship between the 87 Sr/ 86 Sr of the soils and the different parts of the grapevine (root, steam, grape, grape pulp, grape seed, grape skin), and the soil characteristics (soil type, granulometry, root density) that control the 87 Sr/ 86 Sr of the end-products. Results showed that the geological characteristics of volcanic terranes of Italy, and in particular the northwest to southeast 87 Sr/ 86 Sr gradient, are inherited by the wines of each region, such that the wines can be discriminated and authenticated by their isotope ratios. • Sr isotope ratios are reliable tools for characterising the region of production of a wine at a local scale. • Sr isotope signature reflects the initial substrate rock to the overlying soil and ultimately to the wine. • The soil labile fraction is more isotopically correlated to the biological samples produced by the vineyard. • A NW-SE decreasing 87 Sr/ 86 Sr isotope trend is observed for Italian volcanic rocks and wines. • Volcanic Italian wines can be discriminated and authenticated by their 87 Sr/ 86 Sr isotope ratios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".