Nature versus Nurture in the California Wine Industry: The Causes and Consequences of High Brix Grapes and High Alcohol Wine
Bibliographic record
Abstract
The sugar content of California wine grapes has increased significantly over the past 10-20 years, and this implies a corresponding increase in the alcohol content of wine made with those grapes. In this paper we develop a simple model of winegrape production and quality, including sugar content and other characteristics as choice variables along with yield. Using this model we derive hypotheses about alternative theoretical explanations for the phenomenon of rising sugar content of grapes, including effects of changes in climate and producer responses to changes in consumer demand. We analyze detailed data on changes in sugar content of California wine grapes at crush to obtain insight into the relative importance of the different influences. We buttress this analysis of sugar content of wine grapes with data on the alcohol content of wine. * Julian Alston is a professor in the Department of Agricultural and Resource Economics and Director of the Robert Mondavi Institute Center for Wine Economics at the University of California, Davis, and a member of the Giannini Foundation of Agricultural Economics. Kate Fuller is a PhD candidate in the Department of Agricultural and Resource Economics at the University of California, Davis. Jim Lapsley is Adjunct Associate Professor in the Department of Viticulture & Enology at the University of California, Davis and Academic Researcher at the UC Agricultural Issues Center. George Soleas is Vice President, Quality Assurance and Specialty Services, Quality Assurance, Liquor Control Board of Ontario. We are grateful for data provided by the Liquor Control Board of Ontario and Calanit Bar-Am. The work for this project was partly supported by the University of California Agricultural Issues Center. Authorship is alphabetical. Copyright 2010 by Julian M. Alston, Kate B. Fuller, James T. Lapsley, and George Soleas Too Much of a Good Thing? Some wine writers express their dismay Over high alcohol cabernet Burning coal, says Al Gore Not the high Parker score Is the cause of the rising baume Still a 15 percent chardonnay Will be too hot to drink most would say Lower Brix on the vine Spinning tricks with the wine Or a lie on the label might pay
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".