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Record W3011968695 · doi:10.22630/aspe.2018.17.3.44

SOCIO-ECONOMIC IMPACTS OF EPIDEMIC DISEASES OF FARM ANIMALS

2018· article· en· W3011968695 on OpenAlexaboutno aff
Aldona Zawojska, Tomasz Siudek

Bibliographic record

VenueActa Scientiarum Polonorum - Oeconomia · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsBovine spongiform encephalopathyOutbreakEconomic impact analysisAnimal welfareGovernment (linguistics)Economic welfareClassical swine feverEconomic costWelfareEconomicsBusinessDiseaseMedicineBiologyVirologyMarket economy

Abstract

fetched live from OpenAlex

Based on desk research and literature review, the paper identifies the effects of farm animal disease outbreaks from the economic perspective. It provides a brief overview of broad impacts of trans-boundary animal diseases such as Bovine Spongiform Encephalopathy (BSE) and Foot and Mouth Disease (FMD) on the economy and society. It also presents a synthetic summary of the results of several studies dealing with the assessment and estimation of the costs of BSE and FMD epidemics in selected countries. The two epidemics were costly, both in monetary and non-monetary terms. Assessed direct and indirect economic losses were equivalent to several billion US dollars or euro in the countries under consideration. The economies depending on the export of live animals and meat products (e.g. the UK and Canada) were particularly affected. The economic welfare losses from hypothetical FMD outbreak in the USA could exceed a hundred billion US dollars. From the political perspective, government-run policies aimed at controlling and eradicating dangerous animal diseases seem to find the justification primarily in economic rationality or international competitiveness arguments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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