An Economic Survey of the Wine and Winegrape Industry in the United States and Canada
Bibliographic record
Abstract
The wine industry in the United States and Canada is new by Old World standards but old by New World standards. The industry has had several rebirths, so specifying its age may depend on the purpose of the investigation. In the colonial and post-colonial period up through the middle of the 19 th Century, it was a relatively tiny industry with imports accounting for almost all of the still meager consumption of quality wine in the region (Winkler, et al.). There was gradual development in the latter half of the 19 th century, but wine production in the United States and Canada only began to develop significantly with the expansion of the California industry early in the 20 th century (Carosso; Hutchinson). Then the industry needed to be recreated after the prohibition era from 1920 to 1932. More recently, in a sense, the industry was reborn again thirty or so years ago with an aggressive movement towards higher quality. The geography of the industry is relatively simple. Despite some wine and winegrape production in Canada and most states in the United States, California is the location of more than 90 percent of grape crush and about 85 percent of the wine production in North America (Wine Institute). Therefore, most of the discussion of grape and wine production in this
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".