Correlation of different pollution criteria in the assessment of metal sediment pollution
Bibliographic record
Abstract
In this article an assessment of the sediment metal pollution (cadmium, copper, chromium, lead, nickel, zinc) in the Veliki Backi canal (Serbia) was carried out using pseudo-total metal content, contamination factor (CF), pollution load index (PLI) and enrichment factor (EF). The study also encompassed pore-water metal concentrations and an assessment of sediment pollution based on the analysis of simultaneously extracted metals (SEM), acid volatile sulphides (AVS) and the sequential extraction procedure. The concentrations of metals are likely to result in harmful effects based on the comparison with sediment quality guidelines (Dutch, Canadian, US EPA - United States Environmental Protection Agency). The ratio of simultaneously extracted metals and volatile acid sulphides was found to be greater than 1 in only one location, which is already recognized as a place of high risk based on the criteria applied. Other samples had Σ[SEM]/[AVS] < 1, despite their high risk classification based on the applied criteria. According to the sequential extraction procedure, zinc and nickel exhibit high risk in most samples, whereas other metals show low and medium risk. The CF values for Cr, Cu and Zn were > 6 in most samples, which denotes very high contamination by these metals. The PLI values indicated moderate and high pollution. The EF values for all metals studied except for Cd in some cases were >1.5, suggesting anthropogenic impact. The obtained results will be invaluable for future activities regarding sediment monitoring and will facilitate the selection of appropriate criteria when evaluating sediment quality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".