Lessons Learned from the Canadian Cattle Industry: National Animal Identification and The Mad Cow
Bibliographic record
Abstract
Canada implemented a national cattle identification system, led and developed by the industry. Initially a voluntary program beginning in July 2001, it became mandatory in July 2002 and achieved 92-95 percent compliance by that fall. The costs to develop and initiate the system were low; animals are tagged before leaving the farm of origin and the tags are read when the animal dies or is exported. The national identification system did not protect Canadian cattle from a sole case of bovine spongiform encephalopathy (BSE), or Mad Cow Disease, found in the spring of 2003, but it did help speed and lend confidence to the investigation. While the identification system was the objective of the study, the team also reports on how markets and an industry behave in a crisis.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".