Factors associated with in-transit losses of fattening pigs
Bibliographic record
Abstract
Abstract In-transit losses (ITL) in fattening pigs refers to mortality occurring after having left the farm but prior to stunning at the abattoir. The purpose of this observational study was to identify the associations between environmental and truck temperatures, distances travelled, feed withdrawal, farm, transport company and abattoir and in-transit losses of fattening pigs marketed in Ontario, Canada from 2001 to 2004. A prospective study of 104 trips was conducted to determine temperatures inside the truck and identify the factors associated with this. In 2001, ITL was 0.017%, with 75% of producers losing < 5 pigs annually. In-transit losses increased between distances travelled of 590 to 720 km and decreased at distances greater than 980 km. The Pig Comfort Index, a combination of temperature and humidity, was used to identify thresholds of environmental conditions above which in-transit losses increased. The farm at which the pig was raised explained more variation of ITL (25%) than transport company (8%) or abattoir (16%). The within-farm ITL in 2003 had a positive association with those in 2001 and 2002. Withdrawing food prior to transport may decrease ITL on some farms. The temperature in truck compartments holding pigs increased by 0.99°C as the environmental temperature increased by 1°C and by 0.1°C as the relative humidity increased by 1%. Truck temperature decreased 0.06°C for each increase in driving speed of 10 km h−1and increased by 7°C with an increase in pig density from one to 2.6 pigs per m2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".