The Perturbation of Mangla Watershed Ecosystem in Pakistan Due to Hydrological Alteration
Bibliographic record
Abstract
Hydrological regimes influence an aquatic ecosystem’s biotic composition, structure, and functioning. But construction of dams or anthropogenic activities substantially alter the hydrologic regimes. In this study, we used a method named as the “Indicators of Hydrologic Alteration” to examine the degree of hydrologic alteration at seven flow gauge stations in the Mangla watershed. The assessment of alteration is carried out according to the Range of Variability (RVA). This method relies on analyzing hydrologic data obtained from existing measurement points (e.g., stream gauges) within an ecosystem or model-generated data. We used 33 parameters categorized into 5 groups based on magnitude, duration, frequency, timing, and rate of change to characterize hydrologic variation within a year statistically. We then examine the hydrologic perturbations by comparing the measure of central tendency and dispersion for each parameter between the “pre-impact (1967–1994)” and “post-impact (1995–2014)” periods. The results show that within the Mangla watershed, the high alteration was noted in the magnitude of monthly flows and extreme flows at Azad Pattan, Gari Habibullah, Palote and at Muzafarabad stations. The flow at Domel and Kohala stations are found in low hydrological alteration among all groups of indicators. The study indicates that Neelum Basin at Muzaffarabad has significantly high alteration with maximum negative values. On the other hand, a high frequency of alteration observed in the monthly flows and extreme water conditions. Overall, a moderate alteration is observed in the whole watershed, which may produce adverse effects on the aquatic ecosystem of the Mangla watershed.
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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".