Bibliographic record
Abstract
A pet food recall that started in March 2007 has caused widespread fear and consternation among pet owners living in North America, Europe, and other parts of the world. At first, the alerts involved some gravy-based dog and cat food products that had been supplemented with wheat gluten, a concentrated protein derived from wheat flour that was imported from China to Canada. As pets fell ill or died, rat poison was detected in some of the tainted food, but later, melamine, a plastic resin used as a fertilizer, was implicated. It is believed that other additives such as corn, rice, or soy gluten, also used to boost the protein composition of foods, may be similarly contaminated. The recalls later expanded to include a variety of wet and dry foods with glutens possibly contaminated by melamine, cyanuric acid, ammelide, or ammeline. Later concerns focused on these contaminants entering the human food chain after tainted feeds were given to hogs, farmed fish, chickens, and other livestock. As the investigations into these health concerns continue to unfold, readers are advised to track this situation closely by consulting some of the links detailed in this chapter, and to pay close attention to pet food ingredients. Some pet owners have reacted by preparing their own pet foods or switching to more natural products or brands with fewer additives. Readers needing a more complete picture of food safety can consult Internet Guide to Food Safety and Security, published by The Haworth Press.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.283 | 0.134 |
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".