Characterization andmetals fractionation of street dust samples fromTehran, Iran
Bibliographic record
Abstract
ABSTRACT: Fifty street dust samples were collected from Southern and Eastern parts of Tehran, the capital of Iran. Samples were collected during summer under dry and stable weather conditions. Total concentrations of copper (Cu), cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni), zinc (Zn), iron (Fe), manganese (Mn) and lithium (Li) were determined. Ten samples were then selected for characterization, metals fractionation and single stage extraction studies. X-ray diffraction (XRD) and X-ray fluorescence (XRF) were utilized to study the crystalline forms of metals and chemical compositions. In addition, sequential and single extraction procedures were used to determine the concentrations of six heavy metals (i.e. Cu, Cd, Pb, Cr, Ni and Zn), and also the mobility and bioavailability of elements. The risk assessment code (RAC) and IPOLL were employed for the risk assessment and pollution intensity of metals, respectively. Results showed that the single extraction procedure could be used to estimate the total concentrations of most metals as an appropriate alternative for total digestion. The mobility and potential availability of metals in Tehran street dust followed the order of Cd> Zn> Pb> Cu> Ni> Cr. The risk assessment code results showed high risk for Pb, Cd and Zn and low-medium risk for Ni, Cr and Cu, and correlated positively with IPOLL result.
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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 | 0.000 | 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".