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Record W3080846733 · doi:10.3390/ani10091517

Identifying Active Salmonella Infections in Swine Nurseries Using Serology and Bacterial Culture and Evaluating Associated Risk Factors

2020· article· en· W3080846733 on OpenAlexafffund
Saranya Nair, Abdolvahab Farzan, Zvonimir Poljak, Robert Friendship

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of Guelph
FundersOntario Pork
KeywordsSalmonellaSerologyBiosecuritySalmonella infectionBiologyCohortMedicineLogistic regressionMicrobiological cultureVeterinary medicineSalmonella entericaImmunologyAntibodyInternal medicineBacteria

Abstract

fetched live from OpenAlex

The objectives of this study were: to identify nursery cohorts with an active Salmonella infection using combined serological and bacteriological methods, and to try to identify risk factors associated with swine nurseries with active Salmonella spread. Twenty pigs from each of 50 cohorts of weaned pigs from 44 different nursery barns were sampled about the time of weaning and near the end of the nursery stage. Information regarding farm management and biosecurity practices were collected using a questionnaire. Blood samples were obtained at both visits, while rectal swabs were collected at the second visit. An enzyme-linked immunosorbent assay (ELISA) was used to test sera for Salmonella antibodies and rectal samples were cultured for Salmonella. A nursery cohort was identified as having an active Salmonella infection if Salmonella was cultured from one or more of the 20 pigs or if serological evidence suggested exposure to Salmonella. The association between farm-level management covariates and active Salmonella infection was assessed in 46 cohorts using a logistic regression model. Nine of 46 (20%) cohorts produced Salmonella-free pigs. The remaining 37 (80%) cohorts were classified as having an active infection. Examination of risk factors failed to identify how negative and positive nurseries differed.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.502
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.100
GPT teacher head0.319
Teacher spread0.220 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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