Contents of Heavy Metals, Nitrates, and Nitrites in Cabbage
Bibliographic record
Abstract
The contents of lead, cadmium, zinc, copper, iron, manganese, nitrites, and nitrates were determined in six species of cabbage of the cruciferous family obtained from different areas of Poland. The results were analyzed and compared in terms of the effect of local industrial (southern Poland, Katowice) or agricultural (southeastern Poland, Lublin) activities on the amounts of heavy metals in the tested vegetables. While the levels of cadmium, lead, and manganese correlated well with the different industrial levels of the locations, the concentrations of copper, iron, and zinc in the vegetables were not very different between the two cities. All the vegetables could generally be characterized by low levels of cadmium and lead (less than 0.1 mg·kg -1 ), and relatively high levels of zinc, iron, and manganese (3-10 mg·kg -1 ) regardless of location. Among the tested vegetables, Chinese cabbage (Brassica pekinensis Rupr) from Katowice consistently gave higher levels of all the analyzed elements (except zinc) than the same vegetable from Lublin, while the other specimens produced variable data. Red cabbage turned out to contain the highest levels of all contaminants compared to other vegetable species. Nitrate levels in all the Lublin samples were approximately equal, suggesting that the extensive fertilization in the Lublin area produces a uniform background level of these anions. On the other hand, the Katowice samples exhibited quite variable and extreme levels of nitrates and nitrites.
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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 | 0.002 | 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) | 0.000 | 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 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".