Beef cattle welfare in the USA: identification of priorities for future research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it