PSIV-21 Pellet processing reduces variability of corn soybean meal-based diet digestibility in growing pigs
Bibliographic record
Abstract
Abstract This study was conducted to determine the effect of pelleting on the digestibility of corn-soybean meal-based diet in growing pigs. Two trials with 6 pigs cannulated at the distal ileum were conducted. In each trial, pigs were assigned to each treatment following a crossover design. In each experiment, the same diet, composed of corn and soybean meal with 10% wheat from two different feed mills, was served in pellet or mash form. Pelleting allowed an increase in digestibility in one of the trials. Apparent ileal digestibility (AID) of dry matter (DM), crude protein (CP) and digestible energy (DE) were improved with pelleting by 8, 12 and 9% (P< 0.01). The AID of amino acids (AA) was also improved (P< 0.05). Apparent total tract digestibility (ATTD) was increased by pelleting in DM, CP and DE by 5, 7 and 6% respectively (P< 0.01). The digestibility of the mash diet in experiment 1 was lower than in the pelleted diet in the experiment 1 and both diets in experiment 2 as shown by the interaction Pelleting X Trial which was significant for the AID and ATTD of DM, CP and DE (P< 0.01). Therefore, in experiment 1, pelleting allows to improve the digestibility of diet to the same level as in experiment 2. The AID of CP was higher by 37% in the mash diet from the second experiment compared to the one in the first experiment. Even though the same ingredients were chosen in the two experiments, this shows the variability in digestibility existing between different feed mills and ingredient sources. This difference was not observed in pelleted diets. The results obtained in these two trials show that pelleting can reduce the variability of digestibility and then give a good digestibility of diets even if the ingredients are of different quality or sources.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".