Beef cattle welfare in the USA: identification of priorities for future research
Bibliographic record
Abstract
This review identifies priorities for beef cattle welfare research in the USA. Based on our professional expertise and synthesis of existing literature, we identify two themes in intensive aspects of beef production: areas where policy-based actions are needed and those where additional research is required. For some topics, considerable research informs best practice, yet gaps remain between scientific knowledge and implementation. For example, many of the risk factors and management strategies to prevent respiratory disease are understood, but only used by a relatively small portion of the industry. This is an animal health issue that will require leadership and discussion to gain widespread adoption of practices that benefit cattle welfare. There is evidence of success when such actions are taken, as illustrated by the recent improvements in handling at US slaughter facilities. Our highest priorities for additional empirical evidence are: the effect of technologies used to either promote growth or manage cattle in feedlots, identification of management risk factors for disease in feedlots, and management decisions about transport (rest stops, feed/water deprivation, climatic conditions, stocking density). Additional research is needed to inform science-based recommendations about environmental features such as dry lying areas (mounds), shade, water and feed, as well as trailer design.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".