Bibliographic record
Abstract
North American red meat industries are highly integrated. Most of this integration has been driven to maximize efficiency and historically, animals have been cheaper to move to the sources of coarse grain than the transport of feeds to the origin of animals. When a foreign animal disease occurs in a previously disease-free area, certain pre-existing trade patterns may result in the massive killing of healthy animals as a consequence of loss of access to live animal markets in other states or countries. In Canada, time-sensitive and resource demanding livestock, such as early weaned piglets (10 lb; 4.5 kg) and feeder pigs (50 lb; 22. 7 kg), will be critically affected. Cattle movement is far less time-sensitive than swine. Governments of European countries have anticipated welfare slaughter as part of their disease eradication preparedness. The concept of welfare slaughter, and the resource implications thereof, have not been included in current published disease emergency planning documents in Canada or the United States. Public outcry related to a disease eradication crisis will be focused on the animal welfare problem, and not disease eradication. If the disease is foot and mouth or classical swine fever, the nature of the emergency will be focused on animal welfare, not disease eradication. The national veterinary infrastructure in both Canada and the USA currently tasked to prepare for animal emergencies have been planning for the wrong emergency.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".