Journal of Food Law & Policy - Fall 2010
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
illness. 2 It began with a loss of appetite, vomiting, an unquenchable thirst, a constant need to urinate, and after everything had transpired, Andy lost more than ten percent of his weight. 3 After rushing Andy to his veterinarian, Gude and his wife were referred to a clinic fifteen miles away. 4Doctors at the clinic sent a urine sample to a specialized metabolic lab at the University of Pennsylvania.'After waiting for an answer for days, Gude and his wife received Andy's diagnosis: Fanconi syndrome, a rare, often fatal disease that affects the kidneys. 6The alleged culprit: chicken jerky pet treats manufactured in China.'In response to cases like Andy's, the U.S. Food and Drug Administration's ("FDA") Center for Veterinary Medicine ("CVM") has "conducted more than 1,200 tests, visited jerky pet treat manufacturers in China and collaborated with colleagues in academia, industry, state labs and foreign governments."'Despite its investigation, the FDA has failed to pinpoint the origin of the problem. 9Bernadette Dunham, director of the CVM, describes it as "one of the most elusive and mysterious outbreaks we've encountered."' 0Although Andy the terrier was fortunate enough to survive after months of expensive treatments to restore his kidney function," other pets have not been as lucky.As of September 30, 2014, the FDA had received approximately 5,000 reports of pet illnesses, some involving more than one pet, which were believed to be associated with the consumption of jerky pet treats.1 2 The reports involved more than 5,800 dogs, 25 cats and included 2. Brady Dennis, Mystery of Pet Deaths Related to Jerky Treats Made in China Continues to Stump FDA, WASH.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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