Bibliographic record
Abstract
The purpose of this invited review was to identify and discuss (a) the welfare issues that can occur when cull dairy cows are sent for slaughter, (b) factors that can affect the occurrence and severity of these issues and how they relate to on-farm management decisions, (c) measures that can mitigate these issues, and (d) proposals to improve the welfare of cull dairy cows. Peer-reviewed literature, book chapters, reports, and guidance documents were sources of information. Severe welfare issues occur when some cull dairy cows that are not fit for the intended journey are transported to slaughter. These issues are even greater if compromised cows are sent to slaughter via an auction market. The decision to send a cull cow to slaughter needs to be made before the cow becomes unfit for the likely journey. If a cow becomes unfit for transport, it should be euthanized on the farm. Some cull cows arrive at slaughter plants and are observed at markets with distended udders, severe lameness, and disease. The numbers of cows with these severe welfare issues could be reduced by improved on-farm decision making about when and how to manage cows with health issues, when to euthanize sick or injured cows, and when a cow is fit for transport and for sale at a market. Dairy producers need to understand the welfare implications of how they manage their cull dairy cows, and that some changes in their management practices are required. This review identified that research on the assessment of the fitness of cull dairy cows for transport and on the factors that influence their welfare during transport and marketing are required. Research about how to motivate dairy producers to incorporate the welfare implications of their management of cull cows into their culling decisions and how to increase their use of industry recommendations about the care of cull cows are priorities.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".