Dietary Content and Potential Health Risks of Metals in Commercial Black Tea in Kampala (Uganda)
Bibliographic record
Abstract
Tea (Camellia sinensis (L.) Kuntze) is among the most widely consumed non-alcoholic beverages. It is a rich source of essential dietary elements mainly potassium and manganese. Tea may also contain toxic metals such as cadmium and lead which pose a threat to human health because of their toxicity. Twenty samples of commercial black tea in Kampala city were randomly obtained and analysed for potassium, sodium, aluminium, arsenic, cadmium, chromium, copper, iron, mercury, manganese, nickel, lead and zinc using Atomic Absorption Spectrophotometry. Human health risks due to exposure to toxic elements from daily consumption of tea were determined using incremental lifetime cancer risk and non-cancer hazard quotient. Metal contents of black tea sold in Kampala were below international regulatory limits. The estimated daily intake of the elements in two grams of black tea was below the recommended values. Hazard quotient and hazard index were within acceptable range. Total cancer risk levels for all the teas were also within United States Environmental Protection Agency (USEPA) acceptable range. Daily consumption of one cup containing two grams of black tea over a lifetime will promote consumer overall health and wellbeing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".