Balancing Animal Welfare, Human Safety, and Research in Agriculture High Containment
Bibliographic record
Abstract
Abstract Livestock research is paramount to understanding the risks associated with unintentional and intentional introductions of emerging, reemerging, and transboundary animal diseases, including their relationship to both the security of the economy and the nation’s food supply. Research involving large animal species conducted in maximum containment Biosafety Level (BSL)-3Ag and BSL-4 facilities include Health and Human Services and United States Department of Agriculture Select Agents that can have severe consequences on both animal agricultural industry and public health. This themed issue of ILAR spans the spectrum of concerns related to this special niche within the animal research community with an emphasis on a review of available research, current trends, and novel approaches relevant to those conducting large animal research with high-risk agents and those charged with regulating those facilities and programs. Articles are authored by those embedded in the high- and maximum-containment community, directly involved with the work, detailing the unique challenges associated with BSL-3 and BSL-4 livestock research.
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 | 0.058 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".