Heavy metal contamination of animal feedstuffs – a new survey
Bibliographic record
Abstract
Summary Contamination of feedstuffs and ingredients with heavy metals poses a major problem for animal health and the transmission of toxic substances within the human food chain, as these elements can be accumulated into meat, egg and milk products. This paper reports on the levels of the three metals, arsenic, cadmium and lead, that exceed EU permitted levels, within premixes and complete feeds (total of 3417 samples) destined for a variety of animal species and taken from different countries during the period 2009–2016. For this period, 20% of all samples were contaminated at levels above the EU limit. In complete feed, ruminants were at the highest risk of exposure with 47% of samples being contaminated. Lead posed the major risk for minerals and cadmium posed the highest risk for premixes during the sampling period. When compared by country, contamination levels varied widely, with the highest contamination determined for Canada with 50% of samples being above the EU limit. To mitigate problems with heavy metals in animal feeds, due diligence in terms of sampling and testing is a necessary tool to evaluate level of risk. Research has shown that trace minerals can be sources of these heavy metals so the industry should be more diligent regarding their provider's quality guarantees. In addition, feeds identified with contamination should not be fed to animals to ensure that these heavy metal toxins are not passed into edible food products, thus providing safety in the food chain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".