Heavy Metals and Microbial Diversity: A Comparative Analysis of Rivers Swat and Kabul
Bibliographic record
Abstract
Water contamination with heavy metals seriously affects water and sediment quality and may affect the aquatic biota. This study assessed the impact of heavy metals on the morphological characteristics of aquatic microorganisms in potentially contaminated water. Different physicochemical parameters and heavy metals contents were analyzed for toxicological assessment along with microbial diversity in the rivers Swat and Kabul. The pH of River Swat water was neutral to slightly alkaline, while River Kabul was neutral to slightly acidic. The results showed substantial variations in heavy metal concentration across different sampling points. In both River Swat and River Kabul water samples, Cu and Zn concentrations were below the permissible limits for surface and drinking water qualities while the rest of the heavy metals exceeded the permissible limit with Cd being the most abundant heavy metal. Similarly, in sediment samples all the heavy metals were below the permissible limits except for Cd that exceeded the Environment Canada (EC) limits in River Swat and EC and NOVA limits in River Kabul. The rest of the heavy metals concentrations were within the permissible limits, with few exceptions. The results showed that in River Swat, most of the contamination was of geogenic origin, while the main source of contamination in River Kabul was anthropogenic. Results of microbial analysis showed that River Swat has more diversity than River Kabul, which may be due to the low contamination profile of River Swat. It was further observed that high heavy metal concentrations negatively impact the morphological characteristics of microorganisms. The heavy metals concentration and microbial diversity were closely related to each other.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".