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Record W3143696327

The Impact of Foot and Mouth Disease Outbreaks in Taiwan and South Korea on the Red Meat Industries in Canada and the United States

2008· preprint· en· W3143696327 on OpenAlexaboutno aff
Pierre Charlebois, Stephan Gagne

Bibliographic record

VenueRePEc: Research Papers in Economics · 2008
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural economicsOutbreakLiberalizationGeographyAgricultureRed meatBusinessAgricultural scienceEconomicsBiologyFood scienceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

In addition to trade liberalization, other factors have contributed to the strong growth of red meat production in Canada since the end of the 1980s. In particular, the outbreaks of foot and mouth disease (FMD) in Taiwan and in South Korea eliminated two competitors in the Japanese market. This reduction in supply caused an increase in the price of hogs in the United States and Canada of 2.5% and 3% respectively during the 1997 to 2007 period. The higher price stimulated Canadian production by an average of 5%, and by 2% in the United States. Annual agricultural farm receipts from the hog market were greater by an average of $CD 276 million (9%) for a grand total of $CD 3 billion over the 11 years. Moreover, the value added in the red meat processing industry was on average $CD 158 million higher (5%) for a cumulative total of $CD 1.7 billion. Finally, the value of exports of the red meat supply chain is on average $CD 239 million higher (4.4%) for a grand total of $CD 2.6 billion during these 11 years.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.271
Teacher spread0.223 · 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 designObservational
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
Published2008
Admission routes1
Has abstractyes

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