Toxicological impact of water pollutants on DNA and tissues of inhabitant fish Labeo dyocheilus of River Kabul, Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
This investigation evaluated pollution in River Kabul and impact on DNA and histology of intestine, gills, liver, and muscle of Labeo-dyocheilus. Water and fish samples were collected from non-polluted site of Warsak Dam and polluted sites of Amanghar industrial zone and Nowshera city. Sequence of physicochemical parameters in water samples A, B and C was TDS >TSS >EC >TA >Cl >Na >K >pH and heavy metals was Zn >Pb >Cd >Ni >Fe >Mn >Cu >Cr. The parameters in samples A, B and C except TSS were below NEQS proposed limits. The investigation determined geno-toxicological impact of water pollutants in different tissues. DNA damage cells like TCS and comet classes (0, 1, 2, 3, 4) were determined in intestine, gills, liver, and muscle. Sequence of comet classes in tissues was class 0> class 4> class 3> class 2> class 1. Trend of DNA damage in tissues was intestine >liver >gills >muscle. The study investigated histopathological impacts of water pollutants in intestine, liver, gills, and muscle. Trend of lesions in tissues was liver >intestine >gills >muscle. The study confirmed high levels of pollutants like heavy metals and physicochemical parameters in River Kabul, and geno-toxicological and histopathological disorders of water pollutants in Labeo-dyocheilus.
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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 | 0.000 | 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".