168 Evaluation and Improvement of the Nutritional Value of Cereal and Pulse Grains for Swine
Bibliographic record
Abstract
Abstract Feed represents the greatest variable cost of swine production, with feed energy as its largest component. In most swine production systems, cereal grains such as corn, wheat, or barley provide historically the most energy in feed. Cereal grains contain >50% starch and < 20% crude protein. Pulse grains such as field pea, faba bean, and chick pea are now widely grown on the Great Plains for human consumption and crop diversification. Pulse grains contain 30 to 40% starch and 20 to 30% crude protein. Apparent total tract digestibility (ATTD) of starch does not differ between cereal and pulse grain. However, apparent ileal digestibility (AID) of starch is lower for pulse grains; thus, apparent hindgut fermentation of starch is greater. Both AID and ATTD of total dietary fiber are greater for pulse than cereal grains. Calculated net energy (NE) value is greater for cereal than pulse grains. Standardized ileal digestibility (SID) of lysine is greater for pulse than cereal grains. Cereal and pulse grains are milled prior to diet mixing, and particle size reduction can increase ATTD of energy. Whereas steam pelleting may not increase nutrient digestibility of pulse grains, extrusion may increase digestibility of both energy and amino acids. Fiber-degrading enzymes can increase nutrient digestibility of grains by depolymerizing the fiber matrix. In phase-3 nursery diets that are formulated to equal NE value and SID lysine, barley grain can replace wheat grain without reducing growth. Similarly, pulse grains can replace part of cereal grain and soybean meal in phase-3 nursery diets without reducing growth. In conclusion, pulse grains are alternative energy sources to cereal grains but can also replace protein feedstuffs such as soybean meal in sustainable swine feeding programs and provide agronomic benefits like rhizobia N fixation and reduced carbon footprint.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".