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Record W3168795526

Effects of a Traceability System on the Economic Impacts of a Foot-and-Mouth Disease Outbreak

2011· preprint· en· W3168795526 on OpenAlexaboutno aff
Jason Jones, Jared G. Carlberg, Dustin L. Pendell

Bibliographic record

VenueMspace (University of Manitoba) · 2011
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsTraceabilityOutbreakFoot-and-mouth diseaseEpidemiologyAgricultureDiseaseVeterinary medicineEnvironmental healthEconomic impact analysisGeographyMedicineBiologyEconomicsEcologyStatisticsMathematicsVirology
DOInot available

Abstract

fetched live from OpenAlex

The research reported in this paper created an epidemiological foot-and-mouth disease (FMD) spread model for Ontario. Disease simulations were constructed to reflect three levels of the cattle identification and movement recording system. Outputs generated by the epidemiological model are used to calculate the direct disease control costs of a FMD outbreak. Welfare effects caused by a FMD outbreak are also calculated for each level of cattle traceability using an equilibrium displacement model. Parameter sensitivity was tested for both the epidemiological and economic model results. It is found that the benefits to the beef cattle industry of increasing the ability to trace direct animal contacts during a FMD disease outbreak in Ontario are less than the lowest annual cost estimate of a cattle traceability system as estimated by Agriculture and Agri-Food Canada.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.025
GPT teacher head0.193
Teacher spread0.169 · 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 designSimulation or modeling
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

Citations5
Published2011
Admission routes1
Has abstractyes

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