Bibliographic record
Abstract
Exogenous enzymes have been used extensively in the diets of poultry to improve productive performance. Further research, however, is needed to evaluate the efficacy of enzyme use and for the expansion of the use of enzymes to accommodate the wide array of dietary constituents used in poultry feeding programs. The use of effective phytase preparations to improve bird performance and to reduce environmental P pollution has shown less than optimum results, partly due to the potential negative effects of nontargeted dietary fiber components and to confounding influence of inadequate knowledge of accurate P requirements and the tendency for the use of excessive safety margins in diet formulation. Targeting specific nonstarch polysaccharides (NSP) of wheat, barley, or rye with enzyme preparations has proven effective for diets based on these cereals but not for corn- and soybean meal-based diets, primarily due to the differences in constituent NSP. The increased use of whole flaxseed in poultry diets represents an additional research area for effective enzyme development to alleviate potential negative effects of constituent NSP components. Such challenges of the enzyme development process and their outcomes are presented in this publication.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".