Development and Implementation of a Mandatory Animal Identification System: The Canadian Experience
Bibliographic record
Abstract
This article provides a brief history of the animal identification (ID) system that previously existed in Canada along with details on efforts to “reidentify” the country's cattle herd. The current state of ID for various species is summarized, and the state of regulations federally and for major agricultural province are outlined. A short background on the economics of animal ID is provided. Particular attention is paid to the operation of the Canadian Cattle Identification Agency, an industry-government initiative charged with identifying the national cattle herd. The animal ID system in Canada is found to have performed well when called on in times of animal health crises, although there have been notable deficiencies in its performance on occasion. Canada's animal ID system will continue to evolve as new technologies for tagging and database management (among others) are developed. It is expected the system will play an important role in future attempts to exploit traceability for value-added initiatives.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".