Bibliographic record
Abstract
Dear Sir, It is with great interest that I read the Special Report from Health Canada regarding the extra-label drug use in food-producing animals. I applaud the authors for their vision in ensuring the prudent use of drugs in the livestock industry. The report gives the impression that the Government of Canada believes that veterinarians are well-positioned to safeguard the use of pharmaceuticals in the livestock industry, which is similar to the views of the Alberta Veterinary Medical Association. However, this vision seems contrary to the “Own Use Importation” of pharmaceuticals from the United States, whereby veterinarians may not have any input on the use of drugs in the human food supply. This could leave the decision regarding the use of drugs to the livestock producer who may be conflicted in what he/she views as necessary for livestock treatment or production, versus what is in the best interest of the public. If “Own Use” is interpreted to mean that these are animals that will only be consumed by the producer and his/her family, I may be okay with that. If, however, it means that the animals will be consumed by the unknowing public, I am not. It is time for Health Canada to be consistent with their policies.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.030 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".