Pathogen Survival in Swine Manure Environments and Transmission of Human Enteric Illness—A Review
Bibliographic record
Abstract
The influence of zoonotic pathogens in animal manure on human health and well-being as a direct or indirect cause of human enteric illness is examined. Available international data are considered, but the study is focused on the developing situation in western Canada, where it is certain there will be further rapid growth in livestock numbers, particularly hogs. Major pathogens considered are Escherichia coli O157:H7, Salmonella, Campylobacter, Yersinia, Cryptosporidium, and Giardia. Canada is now the leading exporter of pork internationally, but recent increases in production contrast with constant domestic levels of pork consumption and declining levels of foodborne illness caused by pork. Effects of increased levels of manure production are not quantifiable in terms of effects on human health. The presence of major pathogens in manure and movement to human food sources and water are considered on the basis of available data. Survival of the organisms in soil, manure, and water indicate significant variability in resistance to environmental challenge that are characteristic of the organisms themselves. Generally, pathogens survived longer in environmental samples at cool temperatures but differences were seen in liquid and solid manure. Based on actual data plus some data extrapolated from cattle manure environments, holding manure at 25 degrees C for 90 d will render it free from the pathogens considered above.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".