Inference of hydrological modelling and field-based monitoring on dynamics of heavy metals in water of Hindon Basin
Bibliographic record
Abstract
Study region The Hindon basin, crucial for agriculture, faces pollution from industry and farming, affecting the water quality and ecosystems. Study focus This research unravels heavy metal dynamics in uncertain pathways, varying hydrological conditions, and data-restricted basins by integrating a hydrological model with heavy metal mass budgeting. Initially, spatiotemporal meteorological data and landscape properties were utilized to develop a monthly-scale hydrological model for 12 years (2010–2022). Subsequently, heavy metal concentrations were measured in 2023 from rivers, canals, and wastewater drains across the basin. The river flows predicted by the hydrological model were then used to estimate seasonal heavy metal loads based on observed concentrations. New hydrological insights The hydrological activity in the region displayed drier conditions from 2010 to 2023, with declining post-monsoon water balance components in the lower and middle regions. However, in 2023, enhanced monsoon rainfall reversed this trend, improving surface water availability, increasing flow rates, and altering heavy metal dynamics during the post-monsoon season. This shift underscores the role of hydrological uncertainty in heavy metal loads across all surface water bodies. River water contributed the largest share of heavy metal loads (52 %) in both seasons, followed by wastewater (26 %) and canal water (22 %). These findings emphasize the influence of changing hydrological conditions on heavy metal contributions from surface water sources, reducing reliance on direct flow measurements in challenging field environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".