Pesticide residues in botanics used in feed additives: focusing on wild vs cultivable plants
Bibliographic record
Abstract
Dietary inclusion of herbal components in animal feed is gaining interest due to the reduction of some antibiotic use to decrease drug resistance.Obtaining such products relies on their culture or gathering in a wild environment.Nowadays, pesticide use in agriculture is increasing despite different concerns about public health.The present study provides a pesticide residue assessment of herbal components dedicated to feed additive production.A total of 92 samples of different herbal components were analyzed by three private accredited institutions, PRIMORIS (Belgium), PHYTOCONTROL (France) and EUROFINS (France).These analyses were performed by using gas chromatography tandem mass spectrometry (GC-MS/MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS) methods.Data revealed the presence of residues in 63% of the samples with 10% more than the European Maximum Residue Levels (MRLs).Both herbal components, from wild or culture systems, were contaminated in our samples, respectively 65% and 60%.Wild plants from preserved areas such as the Amazonia forest were found to be surprisingly contaminated.In addition to the detection of pesticides in all countries investigated from various continents, 45% of pesticides were not approved by the European Union Commission.This study provides useful information about plantbased additives by giving awareness to all companies involved in this activity.Despite the low incorporation rate of these additives in feed, a regular monitoring strategy should be developed within each company to ensure safe food for consumers at the top level of the food chain.
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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) | 0.000 | 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 | 0.000 |
| 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 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".