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Distilling Grain, Feeding Livestock

2020· book-chapter· en· W4254078313 on OpenAlexaboutno aff
Karl Raitz

Bibliographic record

VenueUniversity Press of Kentucky eBooks · 2020
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsDistillers grainsLivestockAgronomyGeographyAgricultureAgricultural economicsAgricultural scienceEnvironmental scienceBiologyEconomicsBiotechnologyForestry

Abstract

fetched live from OpenAlex

Kentucky’s nineteenth-century distillers used Indian corn as their primary grain, but they also distilled wheat, rye, and barley. Thus, they needed reliable sources of quality grain. Corn became a staple grain, consumed in quantity by farm families and town residents alike. Corn was widely grown in the nineteenth century, but before 1860, only farmers in the Bluegrass region were producing sufficient grain to feed their own livestock, sell to millers for human consumption, and meet distillers’ demands. After the Civil War, corn production increased, and the grain became more widely available for industrial-scale distilling. Wheat and rye were not extensively grown in Kentucky; they were more valuable than corn for foodstuffs and were not favored by distillers. Although Kentucky farmers produced barley, supplies were often deficient in quantity and quality for malting and use by distillers, necessitating its importation by rail from producers on the Great Plains and in the Canadian Prairie Provinces. Distillers fed hogs and cattle on spent grains, or slop, throughout the distilling season, and by season’s end in late spring, the animals had achieved market weight. This was a form of agriculture-distilling complementarity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.003

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 head0.013
GPT teacher head0.159
Teacher spread0.146 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueUniversity Press of Kentucky eBooksSame topicAmerican Environmental and Regional HistoryFrench-language works237,207