Bibliographic record
Abstract
Canada was not the only country dealing with Salmonella contamination in imported frog legs in the 1970s. During the first half of the decade, Salmonella-contaminated frog legs from countries such as Mexico, Japan, Bangladesh, Pakistan, India, and Indonesia accounted for the highest rate of violations of all foods regulated by the U.S. Food and Drug Administration (FDA). This chapter presents a table that lists examples of food-borne disease outbreaks associated with imported foods. Canada exported Salmonella-contaminated chocolate to the United States in 1972 and imported Salmonella-contaminated chocolate from Belgium in 1985; European Union (EU) member countries imported contaminated snack foods from Israel and exported contaminated meat, dairy products, and produce to their fellow members; the United States has imported contaminated produce from Mexico, Honduras, and Guatemala and-twice in the last 5 years-exported contaminated raw almonds to several countries around the world, including Mexico and Canada. The Almond Board of California, an industry trade association, funded research to determine the potential sources of Salmonella in orchards, to understand the ability of Salmonella to survive in the environment, and to develop and validate treatments to kill Salmonella and other pathogens that might contaminate the nuts during harvest or processing. One way for a country to prevent imported foods from causing illness is to embargo imports from high-risk countries or to embargo high-risk foods from any country. Many countries have chosen to embargo imports of beef or live cattle from countries perceived to be at high risk for bovine spongiform encephalopathy (BSE).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".