Supplementary material from "Are regulations addressing farm animal welfare issues during live transportation fit for purpose? A multi-country jurisdictional check"
Bibliographic record
Abstract
Growing animal welfare concerns have pushed some jurisdictions to strengthen regulations addressing live farm animal transportation, but whether they provide satisfactory levels of protection for animals remains to be shown. Using the recent peer-reviewed literature, we identified four major risk factors associated with live animal transportation (fitness for transport, journey duration, climatic conditions and space allowances) and explored how regulations were structured to prevent animal welfare issues in five English-speaking Western jurisdictions (Australia, Canada, New Zealand, the EU and the USA). All legally binding federal regulations were systematically reviewed and compared. Whether these rules were fit for purpose was assessed using the relevant peer-reviewed scientific literature. Our findings indicate that most regulations in most jurisdictions are often insufficient or too vague to be deemed fit for purpose. All five jurisdictions fall short in guaranteeing adequate protection to livestock during transport. Using recent changes as well as future policy proposals currently in discussion, we drew key future directions for regulatory changes that may largely improve the welfare of farm animals during transportation.
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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.800 | 0.332 |
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".