Bibliographic record
Abstract
Editorial| May 01 2022 The Miracle of Castelvetro Lisa Haushofer Lisa Haushofer Lisa Haushofer is a physician and historian of medicine, science, and food. She is currently a senior research associate at the Chair for the History of Medicine at the University of Zurich. Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2022) 22 (2): iv–vii. https://doi.org/10.1525/gfc.2022.22.2.iv Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Lisa Haushofer; The Miracle of Castelvetro. Gastronomica 1 May 2022; 22 (2): iv–vii. doi: https://doi.org/10.1525/gfc.2022.22.2.iv Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentGastronomica Search On the morning of March 4, 2020, wine flowed from the faucets of homes in the small Italian town of Castelvetro di Modena. When residents got up to brush their teeth and wash their faces, they were greeted with a generous flow of bright red, subtly sparkling liquid with notes of ripe berries, toasted nuts, and a hint of ginger. Alarmed at first, they called town officials. As it became clear that the liquid was nothing but Lambrusco and not harmful in any way, some had a free morning tipple. Others gathered as many empty bottles and glass containers as they could find and filled them for leaner days. The “miracle” of Castelvetro lasted about three hours. During that time, a thousand liters of the finest Lambrusco Grasparossa wine (bearing Italy’s second-highest geographical distinction) flowed from the wrong tap (albeit somewhat diluted). In the early days of COVID-19, this unusual... You do not currently have access to this content.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".