ON-FARM FOOD SAFETY GUIDELINES FOR GREENHOUSE VEGETABLES
Bibliographic record
Abstract
The combination of an estimated 2.2 million cases of foodborne illness annually in Canada and high-profile international outbreaks of foodborne illness related to fresh fruits and vegetables has the potential to undermine public confidence. Other factors such as globalization, production efficiency techniques, and the high level of uncertainty surrounding existing and emerging foodborne risks—all coupled with an unprecedented public interest in microbial food safety and dietary concerns—mean that food safety risk management systems must be both scientifically credible and publicly accountable. The 1993 outbreak of renal failure, hemorrhagic colitis and death due to E. coli O157:H7 contamination of hamburgers in the U.S. associated with the Jack-in-the-Box restaurant chain, had an immense impact on public confidence in the safety of meat (Powell and Leiss, 1997). It also fundamentally changed the food safety policies of many farming, processing and retail industries, as well as the activities of public agencies charged with food safety (CODEX, 1996; FSIS, 1994.). In many respects this outbreak was just one more example of what has been known for years: foodborne disease is a serious public health problem and contamination of food animals and their products (meat, milk and eggs) is a major issue for the food animal industry, all the way from "gate
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".