Management of cull dairy cows—Consensus of an expert consultation in Canada
Bibliographic record
Abstract
Many cull dairy cows enter the marketing system and travel to widely dispersed and specialized slaughter plants, and they may experience multiple handling events (e.g., loading, unloading, mixing), change of ownership among dealers, and feed and water deprivation during transport and at livestock markets. The objectives of this study were to describe the diverse management of cull dairy cows in Canada and establish consensus on ways to achieve improvements. A 2-day expert consultation meeting was convened, involving farmers, veterinarians, regulators, and experts in animal transport, livestock auction, and slaughter. The 15 participants, recruited from across Canada, discussed regional management practices for cull cattle, related risk factors, animal welfare problems, and recommendations. An audio recording of the meeting was used to extract descriptive data on cull cattle management and identify points of agreement. Eight consensus points were reached: (1) to assemble information on travel times and delays from farm to slaughter; (2) to increase awareness among producers and herd veterinarians of potential travel distances and delays; (3) to promote pro-active culling; (4) to improve the ability of personnel to assess animal condition before loading; (5) to identify local options for slaughter of cull dairy cows; (6) to investigate different management options such as emergency slaughter and mobile slaughter; (7) to ensure that all farms and auctions have, or can access, personnel trained and equipped for euthanasia; and (8) to promote cooperation among enforcement agencies and wider adoption of beneficial regulatory options.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".