Distribution and Behaviour of Some Trace Elements as a Function of Apple Varieties in Northeastern Romania
Bibliographic record
Abstract
The levels and distribution of 9 trace elements in apples from two orchards in north-east (NE) Romania were measured by means of Atomic Absorption Spectroscopy (AAS) on 42 samples of 9 different apple varieties, with average content ranges of 0.909–4.458 mg·kg−1 Zn, 0.055–0.409 mg·kg−1 Cu, 0.700–2.476 mg·kg−1 Fe, 0.328–0.695 mg·kg−1 Mn, 0.054–0.257 mg·kg−1 Ni, 0.005–0.101 mg·kg−1 Cr, 0.027–0.420 mg·kg−1 Co, 0.413–1.149 mg·kg−1 Pb, and 0.025–0.127 mg·kg−1 Cd. For some apple varieties, Pb contents are 2 times higher than the maximum contents allowed according to national standards, Cd contents are 6 times higher, and in some cases Zn contents also exceed the national threshold, showing preferential accumulation on specific apple varieties. Whilst some research has been carried out on trace element distribution in apples, this study assessed the areal distribution of toxic trace elements in connection to the adjacent roads. The first apple orchard is located near a county road, with reduced car traffic, while the second orchard shares its southern limit with a high-volume traffic road (E583). The results point towards a proportional increase of Pb and, to some extent, of Cd in the samples close to the E583 road in contrast with the other orchard, where no such observation derived. Along the areal distribution of the selected elements, the preferential accumulation of dietary recommended trace elements in different apple varieties was assessed. In matters of daily nutrients content in trace elements, the best sources of Fe, Cu, and Zn in terms of apple varieties are Golden Delicious, Jonathan, and Kaltherer Böhmer.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".