REVIEW: Identification and Traceability of Cattle in Selected Countries Outside of North America
Bibliographic record
Abstract
Animal identification by means of marking animals' bodies was first recorded 3,800yr ago in the Code of Hammurabi, and throughout history, valuable animals such as horses have been identified to prevent thievery all over the world. Today, the reasons for identification of livestock include production management, control of disease outbreaks, establishment of ownership, requirements for export, and consumer demands. Additionally, there are many methods of animal identification and traceability available today including ear tags, tattooing, branding, electronic methods that implement radio frequency identification technologies (such as rumen boluses, ear tags, and injectable transponders), and biometric methods (such as retinal scanning, nose prints, and DNA). The objective of this review is to demonstrate the implementation of bovine animal identification and traceability systems in selected countries outside of North America (i.e., United States, Canada, and Mexico) for the purpose of creating a knowledge base whereby an effective North American bovine animal identification and traceability system may be created and implemented. This review will discuss regulatory requirements of animal identification and traceability in selected countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".