Invited review: Amino acid bioavailability and digestibility in pig feed ingredients: Terminology and application
Bibliographic record
Abstract
In this review, the terminology that is used to describe the bioavailability and ileal digestibility of AA in pig feed ingredients is defined. Aspects of the methodology to establish bioavailability and ileal digestibility values also are discussed, and recommendations about the use of these values are provided. Two main factors can contribute to differences between bioavailability and ileal digestibility of AA. First, some AA, such as Lys, may be absorbed in chemical complexes that preclude their use for metabolism. Second, fermentation in the upper gut may result in a net loss or gain of AA to the animal. In addition, dietary effects on the efficiency of using bioavailable AA intake for tissue growth or milk production should be considered and may be attributed to endogenous AA losses in the hindgut and the metabolic costs associated with endogenous gut protein synthesis and losses. Ileal digestibility values may be expressed as apparent ileal digestibility (AID), standardized ileal digestibility (SID), or true ileal digestibility (TID). These terms are used to specify how ileal endogenous AA losses are reflected in digestibility values. Ileal endogenous AA losses may be separated into basal losses, which are not influenced by feed ingredient composition, and specific losses, which are induced by feed ingredient characteristics such as levels and types of fiber and antinutritional factors. Values for AID are established when total ileal outflow of AA (i.e., the sum of endogenous losses and nondigested dietary AA) is related to dietary AA intake. A concern with the use of AID values is that these are not additive in mixtures of feed ingredients. This concern may be overcome by correcting AID values for defined basal endogenous losses of AA, which yields SID values. Furthermore, if the AID values are corrected for basal and specific endogenous losses, then values for TID are calculated. However, reliable procedures to routinely measure specific endogenous losses are not yet available. It is recommended that basal ileal endogenous losses of AA should be measured in digestibility experiments using a defined protein-free diet and that these losses are reported with observed AID and SID values. It is suggested that SID values should be used for feed formulation, at least until more information on TID values becomes available.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".