Bibliographic record
Abstract
Abstract Pigs are continuously exposed to pathogens and immune-stimulatory antigens that negatively impact animal productivity. In general, this decrease in productivity is the result of reduced feed intake and increased demand for nutrients to mount an immune response. In addition, immune system stimulation alters amino acid metabolism and utilization, with amino acids redirected from growth towards supporting the immune response. A number of previous studies have shown that nutrient requirements for some amino acids, such as methionine, tryptophan, and threonine, are higher in immune-challenged vs. healthy animals, and supplementation with these “functional” amino acids may improve performance and the immune response. In addition to effects on whole-body growth, both dietary composition and immune challenge result in significant physiological alterations to the gastrointestinal tract, including changes in gut motility, permeability, digestive enzyme secretion, absorptive capacity, and mucin production. Alterations to the gut epithelium induced by dietary protein and/or fiber content may lead to increased susceptibility to pathogens and immune stimulation. Functional amino acids have been shown to be important in the maintenance of gut barrier function as well as immune response. An increased understanding of the interaction of nutrition and the pig’s immune response will be a key component in efforts to reduce feed costs and antibiotic use while improving animal robustness and profitability of the swine industry.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".