Examining the Current U.S. Beef Trade Policies Concerning the Testing for Mad Cow Disease
Bibliographic record
Abstract
Despite existing mad cow disease surveillance efforts in the United States, in place since the 1980s, a cow that tested positive for mad cow disease was granted entrance into the U.S. in December, 2003. The cow that tested positive, according to witnesses, displayed no symptoms that are synonymous with advanced bovine spongiform encephalopathy, BSE. This occurrence had detrimental effects on the U.S. beef export market, as many countries banned American beef. Estimates of the damage inflicted reach into the billions of dollars. BSE in the U.S. has the potential of causing damages in other aspects as well. Aside from the fact that BSE is a public health issue, it has caused political rifts between nations, particularly between Canada and the U.S. It can undermine confidence in the USDA and confidence in the governments ability to handle emergencies. BSE can imperil American good that contain beef or beef products. Finally, it can undermine trust in scientists to provide useful guidance. The subtle changes in U.S. BSE surveillance efforts in the 1980’s were greatly surpassed by the changes that were made when a BSE-positive cow was discovered in Washington State in 2003. However, there remains room for much needed improvement in U.S. BSE surveillance efforts. These changes include: increased testing to include all cows slaughtered in the U.S. and all imported beef products, a nationwide animal tracking program, increased proficiency in training of inspectors, and the implementation of strict rules governing the ingredients of animal feed. The implementation of regulations based on economics instead of public health concerns has the potential to leave loopholes in regulations that the BSE agent might exploit. By enacting the recommendations made in this thesis, the U.S. will greatly increased its' odds of stopping the entrance and proliferation of BSE within its’ borders.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".