Traceability in cattle and small ruminants in Canada
Bibliographic record
Abstract
Traceback systems for cattle and small ruminants are of international concern after the outbreaks of bovine spongiform encephalopathy in the European Union and foot and mouth disease in the United Kingdom and South America. Implementation of a national or international identification system depends on meeting a balance between cost, reliability/durability, ease of use, data transfer speed, protection from fraud, avoidance of entry into the food chain and animal welfare issues. As of 1 January 2001, Canada has instituted a national identification programme for cattle, which will have annual operating and administrative costs of Can$0.20 per head, excluding ear tags. The system will provide herd of origin traceback and individual animal identification by ear tags for all beef cattle. A number of identification technologies are available that would have advantages over visual tags, but these are currently too costly without government support (electronic identification, deoxyribonucleic acid [DNA] fingerprinting), too slow (DNA fingerprinting) or have not been tested sufficiently (retinal imaging) to warrant mandatory inclusion in a national traceback/identification system.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".