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Record W4300583088 · doi:10.7591/9780801469008

Wines of Eastern North America

2013· book· en· W4300583088 on OpenAlexaboutno aff
Hudson Cattell

Bibliographic record

VenueCornell University Press eBooks · 2013
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

In 1975 there were 125 wineries in eastern North America. By 2013 there were more than 2,400. How and why the eastern United States and Canada became a major wine region of the world is the subject of this history. Unlike winemakers in California with its Mediterranean climate, the pioneers who founded the industry after Prohibition-1933 in the United States and 1927 in Ontario-had to overcome natural obstacles such as subzero cold in winter and high humidity in the summer that favored diseases devastating to grapevines. Enologists and viticulturists at Eastern research stations began to find grapevine varieties that could survive in the East and make world-class wines. These pioneers were followed by an increasing number of dedicated growers and winemakers who fought in each of their states to get laws dating back to Prohibition changed so that an industry could begin.Hudson Cattell, a leading authority on the wines of the East, in this book presents a comprehensive history of the growth of the industry from Prohibition to today. He draws on extensive archival research and his more than thirty-five years as a wine journalist specializing in the grape and wine industry of the wines of eastern North America. The second section of the book adds detail to the history in the form of multiple appendixes that can be referred to time and again. Included here is information on the origin of grapes used for wine in the East, the crosses used in developing the French hybrids and other varieties, how the grapes were named, and the types of wines made in the East and when. Cattell also provides a state-by-state history of the earliest wineries that led the way

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.613
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.198
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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