Heavy Metals Accumulation in the Edible Vegetables of Lebanese Tabbouli Salad
Bibliographic record
Abstract
Nowadays soil contamination practices create hazardous environment impact that is a vital issue to prevent heavy metals from their integration in the food chain. Plant species selected for this study were the ingredients of the traditional Lebanese salad, Tabbouli, composed of parsley and lemon that were selected from Damour, olive and onion from Ghazir, peppermint and tomato from Akkar and bulgur, to fulfill all the requirements for the assessment of the major physicochemical properties and the contamination levels in vegetables from soils with heavy metal elements. The major physicochemical properties of topsoil including pH, organic matter content method (LOI) and texture showed that studied soils were almost neutral pH of 7.09 with an organic matter content of 4.22% and a texture of sandy loam. Concentration ranges of As, Cu, Fe, Zn, Mn, Ni, Ba, Cd, Co, Cr, Pb and P in studied soil samples were determined and assessed by Inductively Coupled Plasma Optical Emission Spectroscopy, following aqua regia digestion and weak acetic acid digestion. Also, similar measurements on studied plant samples following microwave assisted digestion by HNO3/H2O2 were determined and transfer factors from soils to vegetables were accordingly calculated because of their health risk. Results showed that concentrations of most of studied elements in soil and plant samples were recorded above the permissible limits set by International standards for agricultural soils and by International standards for vegetables FAO/WHO. Furthermore, soil to plant transfer is the major path way of human exposure to potentially toxic elements contamination. Good manufacturing and agricultural practices and safety measurements have to be strictly adapted.
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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 | 0.000 |
| 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".