Investigation of <i>Listeria</i> , <i>Salmonella</i> , and Toxigenic <i>Escherichia coli</i> in Various Pet Foods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), in collaboration with the Food Emergency Response Network (FERN) and its Microbiology Cooperative Agreement Program (MCAP) laboratories, conducted a study to evaluate the prevalence of selected microbial organisms in various types of pet foods. The goal of this blinded study was to help the Center for Veterinary Medicine prioritize potential future pet food-testing efforts. The study also increased the FERN laboratories' screening capabilities for foodborne pathogens in animal feed matrices, since such pathogens may also be a significant health risk to consumers who come into contact with pet foods. Six U.S. Food and Drug Administration FERN MCAP laboratories analyzed approximately 1056 samples over 2 years. Laboratories tested for Salmonella, Listeria, Escherichia coli O157:H7 enterohemorrhagic E. coli, and Shiga toxin-producing strains of E. coli (STEC). Dry and semimoist dog and cat foods purchased from local stores were tested during Phase 1. Raw dog and cat foods, exotic animal feed, and jerky-type treats purchased through the Internet were tested in Phase 2. Of the 480 dry and semimoist samples, only 2 tested positive: 1 for Salmonella and 1 for Listeria greyii. However, of the 576 samples analyzed during Phase 2, 66 samples were positive for Listeria (32 of those were Listeria monocytogenes) and 15 samples positive for Salmonella. These pathogens were isolated from raw foods and jerky-type treats, not the exotic animal dry feeds. This study showed that raw pet foods may harbor food safety pathogens, such as Listeria monocytogenes and Salmonella. Consumers should handle these products carefully, being mindful of the potential risks to human and animal health.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it