Food safety risks and current practices regarding unpasteurized dairy products, juices, and ciders
Bibliographic record
Abstract
Since pasteurization has become widely used, there has been a dramatic decline in foodborne illness associated with dairy products. In the United States dairy products are responsible for less than 1% of reported foodborne disease outbreaks, compared with 25% of all outbreaks in the early 1900s. Unpasteurized dairy products can pose a significant health risk because of their high protein and moisture content, which aids in the multiplication of pathogens. Although less reported, unpasteurized juices and ciders have also been associated with foodborne outbreaks. Health effects from ingestion of contaminated unpasteurized dairy products, juices, and ciders range from fever and gastrointestinal symptoms to organ failure and even death. With the exception of certain aged cheeses, pasteurization of dairy products is required under the Food and Drug Act in Canada. The distribution and sale of unpasteurized milk has been prohibited since 1991; however, personal consumption of raw milk is not prohibited. The sale of unpasteurized juices and ciders is legal in Canada. Microbial standards, labeling requirements, educational programs, nonthermal processing, and other strategies to address microbial hazards in unpasteurized dairy products, juices, and ciders should be explored to determine their efficacy and acceptability. Because of the lack of data regarding foodborne illness attributed to unpasteurized food products in Canada, the population health impacts and Canadian patterns of consumption is unknown. A better system of documenting associated outbreaks is necessary to develop a better understanding of the Canadian situation, so that current policies and practices can be further evaluated.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".