Seasonal Evaluation of Trace Metals in Irrigated Soils at Various Sites in Phuleli Command Area (Sindh), Pakistan
Bibliographic record
Abstract
Phuleli Canal (Sindh) Pakistan is the main source of irrigation water for lower Sindh. However, its quality has been deteriorated by the addition of industrial effluents and city wastes discharged directly or indirectly into canal when it passes through Hyderabad city. For monitoring the effect of this canal water on soil quality of the command area, the present study was designed to evaluate the seasonal variability of trace metals including Cu, Fe, Zn and Mn in the cultivated soil area. The soil samples were collected from different seven sites (reduced distance, RD = 304.8 m) RD-0, RD-30, RD-50, RD-70, RD-90, RD-110 and RD-130) in four seasons (summer, autumn, winter, and spring) and analysed for Cu, Fe, Zn and Mn contents. Then, the obtained results were compared with the maximum permissible values of FAO for agriculture/crop production. Results showed that the Zn contents in soil samples was relatively higher than FAO permissible limits during winter in all soil layers and all sites mostly at upper reach sites of Phuleli Command area. While, the Fe content in soil at all depths was found higher during winter, autumn and spring. Whereas, in summer season it was relatively more in upper soil layers near mid to down reach sites and were not within the permissible limits of FAO. Same was true with Cu and Mn which were also not within the permissible limits of FAO for crop production. So, it was inferred that almost of the locations trace elements in soils found higher under Phuleli Canal Command area were mainly produced from various pollution sources viz, industrial and municipal liquid effluents.
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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 | 0.001 |
| 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".