PSXIII-6 Fat deposition in castrated pigs for production of dry-cured ham: Evaluation of nutritional and genetic strategies
Bibliographic record
Abstract
Abstract Fat deposition is an important trait for the dry-cured ham industry. A minimum ham fat thickness (HFT) is required for adequate processing. The objective of this study was to evaluate two different strategies to meet this minimum requirement, a nutritional and a genetic one. The study involved productive performance of a total of 147,883 animals (barrows and gilts) from 164 farms. The experimental unit for carcass traits harvested was a truck with 200 pigs each. The nutritional strategy consisted in reducing the SID Lys:NE during one month before slaughter in combination with a feed additive to improve meat quality (2500 kcal NE and 8 g SID Lys/kg vs. 2550 kcal NE and 6.5 g SID Lys/kg). Both feeding programs were offered to crossbreds from a lean Duroc sire line with two different synthetic dam lines. The genetic strategy consisted of comparing two crossbred derived from two Duroc sire line (lean vs fat) all fed the same standard finisher diet (2500 kcal NE and 8 g SID Lys/kg). Experimental feeding program impaired FCR by 0.035 kg/kg (P < 0.05), and increased HFT by 0.28 mm (P < 0.01). The fat Duroc sire line impaired FCR by 0.162 kg/kg (P < 0.05) and increased HFT by 0.77 mm (P < 0.001) compared to the lean one. In addition, one of the dam lines showed a greater HFT (P < 0.001) with no effect on FCR. In conclusion, HFT can be increased through nutritional and genetic strategies assuming a negative effect on FCR. The effects were greater by using a genetic strategy (fatter Duroc sire line) than by the nutritional one (decreased SID Lys:NE ratio in a lean Duroc sire line).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".