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Record W3164990677 · doi:10.1002/vetr.349

Porcine reproductive and respiratory syndrome virus seroprevalence in Scottish finishing pigs between 2006 and 2018

2021· article· en· W3164990677 on OpenAlexaff
Carla Correia‐Gomes, Andrew J. Duncan, Allan Ward, Michael C. Pearce, Lysan Eppink, Grace Webster, Andy McGowan, Jill R. Thomson

Bibliographic record

VenueVeterinary Record · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsScience North
Fundersnot available
KeywordsSeroprevalenceVeterinary medicinePorcine reproductive and respiratory syndrome virusHerdMedicineAnimal scienceBiologyVirusSerologyVirologyAntibodyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Porcine reproductive and respiratory syndrome (PRRS) is a major endemic pig disease worldwide and is associated with considerable economic costs. METHODS: In Scotland, three abattoir surveys were conducted in 2006 (158 farms), 2012-2013 (94 farms) and 2017-2018 (97 farms) to estimate seroprevalence to PRRS virus (PRRSV) in commercial finishing pigs. These surveys covered around 79%, 59% and 66% of the Quality Meat Scotland assured farms slaughtering pigs in Scotland in 2006, 2012-13 and, 2017-18 respectively. In the 2006 survey, six pigs per farm were sampled and tested using the CIVTEST SUIS PRRS E/S test. In the 2012-2013 and 2017-2018 surveys, 10 pigs per farm were sampled and tested using the IDEXX PRRS X3 Ab test. A farm was considered positive if it had one or more seropositive samples. RESULTS: The prevalence of positive farms was 45.6% (95% CI: 38.0-53.4), 47.8% (95% CI: 38.1-57.9) and 45.4% (95% CI: 35.8-55.3) in the 2006, 2012-2013 and 2017-2018 surveys, respectively, and 70%-75.5% farms did not change their status between sampling periods. CONCLUSION: The prevalence of PRRSV exposure in Scottish pig herds was high and changed little from 2006 to 2018. These surveys have informed planning for a prospective PRRS control programme in Scotland.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.177
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

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

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 head0.073
GPT teacher head0.271
Teacher spread0.199 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

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