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Record W2161257682 · doi:10.1017/s0950268811000628

Environmental characteristics associated with campylobacteriosis: accounting for the effect of age and season

2011· article· en· W2161257682 on OpenAlexafffund
Julie Arsenault, Pascal Michel, Olaf Berke, André Ravel, Pierre Gosselin

Bibliographic record

VenueEpidemiology and Infection · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsInstitut National de Santé Publique du QuébecCentre hospitalier universitaire de QuébecCegep de Saint HyacintheUniversity of GuelphUniversité de MontréalPublic Health Agency of Canada
FundersCanadian Institutes of Health Research
KeywordsCampylobacteriosisEnvironmental healthMedicineGeographyEnvironmental scienceBiologyCampylobacterGenetics

Abstract

fetched live from OpenAlex

Campylobacteriosis is a leading cause of acute bacterial gastroenteritis. An ecological study was undertaken to explore the association between environmental characteristics and incidence of campylobacteriosis in relation to four age groups and two seasonal periods. A multi-level Poisson regression model was used for modelling at the municipal level. High ruminant density was positively associated with incidence of campylobacteriosis, with a reduced effect as people become older. High poultry density and presence of a large poultry slaughterhouse were also associated with higher incidence, but only for people aged 16-34 years. The effect of ruminant density, poultry density, and slaughterhouses were constant across seasonal periods. Other associations were detected with population density and average daily precipitation. Close contacts with farm animals are probably involved in the associations observed. The specificity of age and season on this important disease must be considered in further studies and in the design of preventive measures.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.028
GPT teacher head0.233
Teacher spread0.206 · 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

Citations28
Published2011
Admission routes2
Has abstractyes

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