Bibliographic record
Abstract
Abstract Fiber is one of the four macronutrients that yield dietary energy for pigs (others are starch, fat, and protein). To yield energy, fiber must be fermented (primarily in the hindgut) by microbes producing volatile fatty acids (VFA). Price increases for traditional feedstuffs such as cereal grains and protein meals have stimulated the pork industry to consider dietary inclusion of fibrous co-products that are produced when grain is processed into human food, fuel, and bio-industrial products. High fiber co-products include distillers dried grains with solubles (DDGS), oilseed meal, expeller, and cake, and co-products from flour milling. As omnivores, pigs are ideally suited to convert these non-human edible co-products into high quality food animal protein. Thereby, co-products can partially offset increases in feed cost provided their price is competitive per unit of net energy or digestible lysine, but also present risks and feeding challenges. Effects of feeding high fiber co-product may depend on diets being balanced for energy value or not. In weaned pigs, high fiber diets were thought to reduce feed intake, and thereby hinder energy intake during the energy-dependent phase of growth. However, such a relation is not solid across the spectrum of dietary fiber. Fiber characteristics play a role in gut health and early development. High fiber diets have lower energy digestibility and concurrent lower feed efficiency, but increased feed intake may maintain growth. In growing-finishing pigs, high fiber diets increase viscera mass, and thereby reduce dressing percentage. In restricted-few sows, fiber and produced VFA play a role in reaching satiety responses. In conclusion, depending on the price of high fiber co-products, high fiber diets may be part of a range of solutions to reduce the feed cost, and may thereby support economically-sustainable pork production.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".