Metal Concentration in Commonly Used Medicinal Herbs and Infusion by Lebanese Population: Health Impact
Bibliographic record
Abstract
The levels of K, Ca, Fe, Zn, Cu, Mn, Pb, As, Cd, Cr, and Pb in commonly used medicinal herbs and their infusions by Lebanese were analyzed by EDXRF and ASS techniques, respectively. The order of metal contents in the herbs was found to be: K (6990-19850 ug/g) > Ca(1630-14450 ug/g) > Fe(80-3650 ug/g) > Mn (28-458 ug/g) > Zn (23-108 ug/g) > Cu (5-71 ug/g) > Cr (3.1-55 ug/g) Pb> (1.1-10.3 ug/g) > As (nd-10.8 ug/g) > Cd (nd-1.7 ug/g). The mean levels of toxic metals in herbs Pb, As and Cd were below WHO permissible levels. Cluster analysis indicated metals are most probably in plants due to wastes disposal and irrigation with contaminated wastes and/or from atmospheric waste particulates. The levels of Mn, Cr, Pb and As in herbal infusions were found to be higher in soaked than boiled preparations and correlated with Fe, while Zn and Cu levels were higher in boiled infusions. The highest weekly intake from herbal infusions of toxic metals Cr (492.8 ug), As (77.0 ug), Pb (291.2 ug) and Cd (19.0 ug) were below the recommended permissible tolerable weekly intake respectively 1260 ug, 900 ug, 1500 ug, and 150 ug. Therefore, the consumption of these traditional medicinal herbs does not pose any health risk provided full compliance with recommended daily doses.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".