Replication Data for: Livestock hauler and dairy farmer perspectives about cull dairy cattle transport and cattle transport regulations in British Columbia, Canada
Bibliographic record
Abstract
Dairy cows are usually culled and transported from the farm when they no longer meet the farm’s standards for production or are not needed for milk production. Some cows are transported while in poor condition and may deteriorate further during transport. In February 2020, Canadian federal animal transport regulations were revised with the aim to minimize risks to livestock during transport; changes that may impact cull dairy cows included defining compromised cattle and limiting their maximum transport time. This study conducted semi-structured interviews with dairy farmers (n=6) and cattle haulers (n=4) in British Columbia, Canada, to gain an in-depth understanding of the effect of the regulations on their practices when shipping and transporting dairy cows to slaughter. Interviews were transcribed in Otter.ai and thematically coded in NVivo 12. While farmer and hauler participants recognized the importance of animal welfare during transport and described practices such as shipping mobile animals to reduce the risk that cows would become non-ambulatory during transport, they also described little change in shipping and transport practices due to the new regulations. Among interviewed participants, barriers to compliance with the regulations appear to be low knowledge of, and mixed or negative attitudes towards the regulations. Participants also described how they felt a lack of communication along the transport chain and limited transport and slaughter infrastructure made compliance difficult. Possible suggestions to remedy these barriers include providing educational resources about the regulations and encouraging communication about cow fitness for transport between responsible parties in the transport chain.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.005 |
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".