Do contamination of and exposure to chicken meat and water drive the temporal dynamics of<i>Campylobacter</i>cases?
Bibliographic record
Abstract
Campylobacteriosis, the most frequent bacterial enteric disease, shows a clear yet unexplained seasonality. The study purpose was to explore the influence of seasonal fluctuation in the contamination of and in the behaviour exposures to two important sources of Campylobacter on the seasonality of campylobacteriosis. Time series analyses were applied to data collected through an integrated surveillance system in Canada in 2005-2010. Data included sporadic, domestically-acquired cases of Campylobacter jejuni infection, contamination of retail chicken meat and of surface water by C. jejuni, and exposure to each source through barbequing and swimming in natural waters. Seasonal patterns were evident for all variables with a peak in summer for human cases and for both exposures, in fall for chicken meat contamination, and in late fall for water contamination. Time series analyses showed that the observed campylobacteriosis summer peak could only be significantly linked to behaviour exposures rather than sources contamination (swimming rather than water contamination and barbequing rather than chicken meat contamination). The results indicate that the observed summer increase in human cases may be more the result of amplification through more frequent risky exposures rather than the result of an increase of the Campylobacter source contamination.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".