Analysis of some heavy metals in foodstuffs contaminated with pesticides using a developed spot-test method
Bibliographic record
Abstract
The need for food security is a call for a simple and quick spot-test that can be used for the detection of contaminated grains and foodstuffs. Mostly, stored grain foodstuffs contained heavy metals due to metallic pesticides applied against pest infestation and environmental metallic contact during production processes. Foodstuffs samples of white beans, red Guinea corn, and white maize corn were purchased from five markets in Makurdi Town. Pesticide residues were extracted from the samples with hot, distilled, and deionized water in a pressure hot water extraction system (PHWES), respectively. The water extract was used to assess the presence of metallic pesticide contents of some heavy metals with the spot-tests developed. The results show that the water extract from grain foodstuffs contained Al, Fe, and Zn in almost all the samples. This could be a result of the predominant use of metallic pesticides like aluminium phosphide insecticide for the storage of grain foodstuffs. The spot-test developed is a simple and veritable technique for checking metallic pesticides as contaminants on/in foodstuffs at the preventive stage. This spot-test will help to curtail the consumption of metallic pesticides from grain foodstuffs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".