Bibliographic record
Abstract
The paper provides an overview of the knowledge of the options of the use of selected feed additives in the feeding of sows and young pigs. Feed enzymes, organic acids, probiotics, prebiotics and phytobiotics are the most commonly used feed additives for these technology groups. Nowadays, in pig breeding and farming, it is not possible to achieve high production performance in the herd without the use of specialised feed additives. They are added to the feed ration always in small quantities, and the effects of their use are often clearly noticeable. They improve the flavour and digestibility of the feed, and thereby have a favourable effect on feed intake, piglets weight gain and sows milk yield, boost pig immunity and displace pathogens from the gastrointestinal system, stimulating the growth and development of the beneficial bacterial microflora. Fed to lactating sows, they have a positive effect not only on the sows, but above all on the growth and development of their litters. This allows maximising the weaning weight of piglets and improving their health and weight gain at further fattening stages. All this contributes to improved production performance and should encourage breeders to enrich pig feed with appropriate specialised feed additives.
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) | 0.001 | 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".