Bibliographic record
Abstract
Abstract Exogenous enzymes are routinely added to diets for non-ruminant animals, mainly to help enhance energy and nutrient utilization, thus contributing to efficient and sustainable production systems. Also, feed enzymes allow for effective utilization of non-traditional feedstuffs (e.g. co-products) in non-ruminant diets, with potential to mitigate feed cost. In addition to increased nutrient utilization, however, feed enzymes, through their impact on the gastrointestinal environment and microbial composition, can have a profound effect on indices of gut health and function. These effects may explain the reported reductions in medication costs and variability in animal performance and mortality rates observed with dietary enzyme supplementation. By acting on their target substrates, feed enzymes reduce the availability of non-digested substrates and in the case of carbohydrate-degrading enzymes can produce short-chain oligosaccharides with potential prebiotic effects. These changes are known to modulate the gut microbiome in favor of bacteria associated with a healthy gut, while reducing the population of pathogenic bacteria, which explains the reduced incidences of diarrhea that have been attributed to enzyme supplementation. Also, feed enzymes may reduce oxidative stress and enhance the functional capacity (e.g. maintaining barrier function) of the gastrointestinal tract. Feed enzymes by themselves may never be a single solution to mitigate gut health challenges in livestock production systems, but clearly there is a mounting body of evidence to support the role of feed enzymes in this regard. Therefore, their benefits will be maximized when used as part of an integrated solution approach for containing enteric pathogens of economic importance and eliminating the negative effects of dietary components with potential to compromise intestinal integrity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".