Use of Prebiotics and Probiotics in Heat Stress
Bibliographic record
Abstract
Heat stress (HS) negatively effects the productivity of livestock reducing milk, meat and egg production.Mitigating the detrimental effects of HS is crucial for enhancing productive performance of animals.Dietary supplementations that include prebiotics and probiotics enhance livestock productivity.Prebiotics like fructooligosaccharides, inulin and xylooligosaccharides promote the growth of beneficial gut bacteria that lead to better digestion and improved nutrient absorption.Probiotics like Lactobacillus, Bifidobacterium and Saccharomyces Cerevisiae improve gut health resulting in greater productivity.Addition of prebiotics and probiotics to the livestock feed can reduce negative effects of HS improving milk yield, meat quality and egg production.This approach can help farmers meet competitive market demands and increase their income.Implementing these strategies promotes sustainable livestock production, improves food security and supports the livelihoods of farmers.By implementing this approach a more resilient and productive livestock industry can be ensured that will ultimately contribute to the well-being of both farmers and consumers.This approach can have a positive impact on livestock industry ensuring a brighter future for farmers and consumers.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".