Bibliographic record
Abstract
Transport stress is both an animal welfare issue, resulting in animal losses and fatigued animals at slaughter, and a meat quality issue, resulting in carcass depreciation and meat quality defects. Both issues may result in significant economic losses for the industry. The welfare of pigs during transport (by road and by air) depends on many interacting factors, such as the condition of the animal, ambient temperature and relative humidity, loading density and time in transit, among other factors. Death losses during road and air transport of pigs can vary from a low of 0.03% under good conditions to a high of 3% under overcrowding conditions. The results arising from road transportation studies run over the last years have shown that both short and long journeys may be stressful for pigs and that poor vehicle design reduces ease of loading/unloading and thermal comfort of pigs during transport, sometimes resulting in meat quality defects. These effects can be exacerbated by the use of stress-susceptible pigs and vulnerable animals, such as piglets and cull sows, and by insufficient space allowed for pigs to lie down, drink, thermoregulate and rest during transport. These factors can also account for animal losses during air transportation of breeding pigs, and significantly more research in this area is needed to improve the pre- and in-flight practices and conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.040 |
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".