Bibliographic record
Abstract
Development of a suitable on-farm euthanasia plan should be part of the regular veterinary-client discussions that occur during ongoing herd or flock assessments. Close monitoring of behavior by those skilled in animal husbandry can lead to early identification and segregation of sick or unfit animals. Clinical endpoints and decision trees should be developed and discussed with clients to ensure that animals that do not improve after suitable therapy and monitoring are rapidly and humanely euthanized using appropriate procedures. Personnel who perform on-farm euthanasia procedures should be trained in the techniques. Euthanasia, derived from the Greek terms “eu” and “thanatos,” means a “good death,” and in the context of veterinary medicine, the term refers to killing animals in as painless and stress-free a manner as possible. It is not a step taken lightly by those in this profession but a responsibility that veterinarians undertake to end animal suffering and distress. In food animal practice, veterinarians are typically not on a farm on a daily or even weekly basis, and euthanasia is a task that we often must delegate to our clients and their employees. For this reason, on-farm euthanasia plans should be developed and regularly discussed with clients as part of the overall herd or flock health management program. In addition to optimizing herd or flock productivity, a properly designed and implemented euthanasia program demonstrates compassion for the animals and will help to address public expectations for enhancing food animal welfare.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".