Tracing and tracking wine bottles: Protecting consumers and producers
Bibliographic record
Abstract
The effective tracking and tracing of wine bottles is critical to ensure consumers are receiving high quality wine from the place of origin that is stated on the label and produced from grapes grown in that place. Wine production and its supply chain are controlled by different laws around the globe. From the International Organization of Vine and Wine (OIV) to the European Union (EU) and other national governments, suppliers and producers are required to provide specific documentation as the wines make their way to consumers. However, the wine industry loses billions from counterfeit wine and illicit trade. That is why the improvement of the methods applied to verify the origin and the quality of wines is important to protect wine consumers and producers. This short presentation explores what members of the Wine Origins Alliance (WOA) are doing within their respected regions to effectively trace and track their wine bottles along the entire value chain, with intelligent labeling and data recording through effective technology. Specifically, WOA provides case studies from its members that give an overview of the methods they have implemented (or are working to implement) to ensure consumers know the true origins of the wine. Their commitment to quality, traceability, and transparency are the very reasons why these regions are considered among the most renowned across the globe. Below are a few examples of the case studies that will be presented. * Chianti Classico. All the wines can be traced from the vineyard to the bottle as the entire production is monitored and recorded. Each bottle must be adorned with a government-issued label on the bottle neck, which contains an alphanumeric code that consumers can use to access the wine’s official chemical analysis and quantity bottled on the open database located on the Chianti Classico website. * Champagne. The General Syndicate of Winegrowers in Champagne (SGV) contracted with Advanced Track & Trace to supply the CLOE caps, which feature a unique serialized code and hologram. A QR code customized to the Champagne grower’s visual identity, which appears on the exterior of the cap, offers customers “access to each bottle's unique information, concealed on the inside of the cap. That includes a serial number, signature, message and illustration of the brand, as well as the ability to check the bottle's origin.” *Rioja. All wine bottles produced in the region are required to include numbered seals for specific zones or municipalities. But, in the Rioja Alta zone, producers have been using artificial vision to photograph each bottle, scanning the code and marking it on the bottle with ultraviolet (UV) link and integrating it into each winery’s computer systems, allowing wineries “to identify and monitor each and every bottle individually, from the moment the wine is labelled until it is delivered to every client, distributor or importer anywhere in the world.”
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".