Bibliographic record
Abstract
Research Article| August 01 2023 From a Hailstorm: Vines, Wines, and Factories in the Alto Piemonte Daniel E. Bender Daniel E. Bender Daniel E. Bender is the Canada Research Chair in Food and Culture and a professor of food studies at the University of Toronto. He is the author, most recently, of The Food Adventurers: How Around-the-World Travel Changed the Way We Eat (Reaktion Books, 2023). He is a WSET Diploma Candidate and a Certified Specialist in Wine. daniel.bender@utoronto.ca Search for other works by this author on: This Site PubMed Google Scholar daniel.bender@utoronto.ca Gastronomica (2023) 23 (3): 7–20. https://doi.org/10.1525/gfc.2023.23.3.7 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Daniel E. Bender; From a Hailstorm: Vines, Wines, and Factories in the Alto Piemonte. Gastronomica 1 August 2023; 23 (3): 7–20. doi: https://doi.org/10.1525/gfc.2023.23.3.7 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 I’m trying to see the forest differently. Facing west, the forest undulates over hills. Turn north, and in the clean morning July air, I can see the Italian Alps. Despite the 2022 summer heat wave and a drought that began with a parched winter, there is still snow at the highest altitudes. Face south and the hills abruptly flatten into Po River Valley plains, strikingly verdant with irrigated rice paddies. East, a small, ordered vineyard, just a few hectares, disrupts the tangle of forest green. I am an outsider here in Lessona, a wine denomination in the larger Alto Piemonte wine region in the northeastern reaches of Piemonte in Northern Italy. My host is a sommelier employed by one of the region’s most notable wineries. In her mind’s eye, and then in her description aloud, she traces the boundaries where the vines grew a century ago. This landscape, now obscured... You do not currently have access to this content.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".