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Record W1589600697 · doi:10.1111/cjag.12041

Economic Welfare Impacts of Foot‐and‐Mouth Disease in the Canadian Beef Cattle Sector

2014· article· en· W1589600697 on OpenAlexvenueaboutno aff
Peter R. Tozer, Thomas L. Marsh, Evgeniy V. Perevodchikov

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsOutbreakWelfareFoot-and-mouth diseaseConsumption (sociology)Production (economics)Animal welfareEconomic impact analysisBusinessAgricultural economicsGovernment (linguistics)EconomicsNatural resource economicsPublic economicsMedicineMarket economyMicroeconomicsBiology

Abstract

fetched live from OpenAlex

A foot‐and‐mouth disease outbreak, although having a low probability of occurrence, results in losses of export markets and introduces considerable potential disease management and eradication costs. We develop a dynamic model that integrates beef cattle production, disease dissemination, domestic consumption, and international trade and captures the intertemporal economic welfare impacts of mitigation measures. The model is applied to the case of a hypothetical outbreak in Canada to capture changes in producer profits, consumer price, and government costs due to the outbreak and sums these to measure changes in total economic welfare of the beef industry. Mitigation scenarios are reported for stamping‐out, movement controls, vaccination, and preemptive slaughter.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.174
Teacher spread0.152 · 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

Citations16
Published2014
Admission routes2
Has abstractyes

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