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Record W7073556180

Seroprevalence of Lawsonia intracellularis in different swine populations in 3 provinces in Canada

2007· article· en· W7073556180 on OpenAlexaboutno aff

Bibliographic record

VenueEurope PMC (PubMed Central) · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsnot available
Fundersnot available
KeywordsSeroprevalenceLawsonia intracellularisHerdPig farmingSerologyIndirect immunofluorescence
DOInot available

Abstract

fetched live from OpenAlex

Porcine proliferative enteropathy caused by Lawsonia intracellularis is an important enteric disease in swine throughout the world. Information regarding the distribution of this pathogen in Canadian swine herds would be beneficial for the creation of control protocols. Pigs from Ontario, Quebec, and Alberta were tested by using an indirect immunofluorescence assay for antibodies to L. intracellularis. Pig seroprevalence was calculated as the proportion of pigs positive from total pigs tested in the targeted population. Seroprevalence (± standard error [s(χ̄)]) in market hogs in Ontario from farrow-finish (FF) farms and finishing (FIN) farms were significantly different at 77% (s(χ̄) = 7%) and 29% (s(χ̄) = 15%), respectively. Seroprevalence for sows and gilts in FF and farrowing and nursery (FAR + NUR) farms in Ontario were 90% (s(χ̄) = 3%) and 93% (s(χ̄) = 6%), respectively. Seroprevalence in breeding females in Quebec from FF and FAR farms was 82% (s(χ̄) = 5%) and 87% (s(χ̄) = 3%), respectively. Seroprevalence (57%, s(χ̄) = 8%) in finishing pigs in Alberta from FF farms was significantly different from that of multisite (MS) farms and FIN farms, 6% (s(χ̄) = 6%) and 9% (s(χ̄) = 5%), respectively. Lawsonia intracellularis appears to be widespread in Canada and the seroprevalence on FF farms is higher than that on FIN and MS farms, possibly due to the presence of breeding females or management differences.

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 score0.136
Threshold uncertainty score0.465

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 head0.015
GPT teacher head0.195
Teacher spread0.180 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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