The impact of dietary pea starch and particle size on gastric ulcers and performance in finishing pigs
Bibliographic record
Abstract
Pea protein production in Canada continues to grow and industries are searching for outlets to utilize pea by-products. The major by-product of pea protein production, air-classified pea starch (ACPS) may be an energy-dense and sustainable alternative to cereal grain in swine diets. However, this by-product has a fine particle size that may induce or enhance the severity of gastric ulcers, leading to reduced growth and increased mortality when fed to pigs. In the following series of experiments, the effects of increasing inclusion levels of ACPS and diet particle size on gastric ulcers and growth performance were investigated. Study one determined that pigs maintained growth and there was only a tendency for an increase in gastric ulceration when finishing pigs were fed diets with up to 40% ACPS. The objective of study two was to determine if the effects of particle size in the diet differed between ingredients (corn vs. pea). The inclusion of ground corn or pea or 20% of either corn starch or ACPS in diets for finishing pigs was utilized to determine effects on gastric ulcer incidence, growth performance, and nutrient digestibility in finishing pigs. Pigs fed the diets exhibited similar gastric ulcer incidence and growth performance. Nutrient digestibility (apparent total tract digestibility dry matter, total starch, and gross energy) and therefore, energy values (digestible and net energy) were decreased only when pigs were fed ACPS diet. In conclusion, feeding up to 20% ACPS diets to finishing pigs was not detrimental and may benefit pork producers as an alternative ingredient to achieve a more sustainable production.
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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 | 0.000 |
| 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".