Water quality and Human Health Risk Assessment: a case study of the Czarna Przemsza River source in Zawiercie, Poland
Bibliographic record
Abstract
The water quality of the Czarna Przemsza River source in Zawiercie was investigated in four sampling campaigns in years 2016 and 2017. Values of 62 indicators (physico-chemical, inorganic, organic, and biological) were compared with permissible limits for drinking water according to Polish legal acts and EU, WHO, USEPA, and Canadian guidelines. The water quality was determined as below good because of EC values and As, Ca, Cd, Cr, Cu, Se, and Zn concentrations. Water was also determined as non-potable due to exceeded concentrations of Al, As, Cd, Cr, Fe, K, Ni, Pb, Sb, and Se. The Human Health Risk Assessment for Zawiercie’s inhabitants was carried out, while water from the source is consumed regardless of its quality. The mean estimated daily intake (EDI) values exceeded Minimal Risk Level values forCr, Cd, As, Se, and Cu. The hazard index (HI) values pointed very high total non-carcinogenic risk in residential exposure scenario generated mainly by As, Cr(VI), Tl, Pb, MCPA, Se, and Cd. Water intake scenario based on questionnaire surveys pointed that risk for inhabitants existed (HI >1). The total carcinogenic risk (Rt) values were at the unacceptable level and decreased in the following order: Cr(VI) > As > Pb.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".