REVIEW: Animal Identification Systems in North America
Bibliographic record
Abstract
The threat of a livestock disease outbreak or other animal health events in North America is real. However, predicting both the timing and severity of an outbreak can be extremely difficult. Animal identification and traceability programs can help limit the spread of disease. The overall objective of this review is to evaluate and compare animal identification and traceability systems in North America. Mandated animal identification programs, which exist for Canadian cattle and sheep and Mexican cattle, are designed to control and eradicate trade-limiting diseases and to maintain or gain access to international markets. In contrast, the United States has chosen to implement the National Animal Identification System as a voluntary program for cattle, sheep, and swine. However, the US sheep industry has operated with a mandatory National Scrapie Eradication Program since 2001, and the US pork industry has independently implemented a mandatory swine premises registry, which targeted 100% compliance by December 31, 2007, and a mandatory swine identification program targeting full compliance by December 31, 2008. Likewise, the Canadian National Hog Traceability and Identification System will become a mandatory program in 2008. It is recognized that a country's ability to respond to an animal disease outbreak is greatly enhanced with the implementation of a national animal identification program.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".