Micotoxinas em silagem
Bibliographic record
Abstract
The silage process consists of preserving green forage. However, errors in operations during harvesting, or even at the opening of the silo, can result in colonization of fungi and production of mycotoxins. Therefore, it is important to develop strategies to mitigate the negative effects of mycotoxins in the feeding of dairy cows. The objective was to review the literature on the contamination of silage by mycotoxins, including predisposing factors for contamination and ways of prevention and mitigation. The main environmental conditions that favor mycotoxin synthesis are temperature, pH and water activity. In addition, factors linked to the operation, such as delayed harvesting, delays in sealing the silo, the density of compaction or the use of damaged seals also favor fungal growth. The control of these processes in silage aims to avoid contamination by toxinogenic fungi. However, current control strategies are not entirely effective. Some safe and relatively economical measures are the use of mycotoxin adsorbents or bacterial inoculants, which can be used to reduce the absorption of mycotoxins in the gastrointestinal tract.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".