Pig-level risk factors for in-transit losses in swine: a review
Bibliographic record
Abstract
In-transit losses (ITLs) of market weight pigs are defined as pigs that die and (or) pigs that become nonambulatory (NA) during the process of loading and shipping from the farm to the abattoir. Annual rates of transport mortalities are low relative to the number of pigs transported to slaughter annually but are highly variable between countries and even between abattoirs within countries. In-transit losses are not fully explained by the most commonly cited risk factors, such as environmental temperature, stocking density, and journey length and other risk factors must be considered. Low numbers of ITLs compared with the large number of pigs shipped each year imply that individual pig factors should be given greater consideration. Pig health pertaining to ITLs is not well studied and post mortems are rarely completed on ITL pigs. In particular, compromised cardiac function combined with a limited ability for cardiac compensation may predispose pigs to ITLs as a result of the exertion experienced during sorting, loading, and transport. Varying stages of cardiac compromise could explain the variable nature of ITLs. Future research should focus on investigating the health conditions which could make a pig more susceptible to death or becoming NA during transport.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".