Surface Water Quality Appraisal and Suitability Assessment for Designated Uses along the Brahmani River Basin, Odisha, India
Bibliographic record
Abstract
The Brahmani River in Odisha supports regional agriculture, industry and domestic supply but human activities are mounting pressures on the water quality of the river. The paper measures the spatio-seasonal variability in the surface-water quality between 2017 and 2024 by using the samples obtained in 12 monitoring stations during pre- and post-monsoon periods. The main parameters that were examined to describe hydrochemistry and their suitability for use were core physico-chemical parameters (pH, dissolved oxygen, biochemical and chemical oxygen demand, total dissolved solids, major cations, and anions). The overall status was calculated in terms of the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), and agricultural usability was assessed using common irrigation indices, which are sodium adsorption ratio (SAR), soluble sodium percentage (SSP), permeability index (PI), Kelly ratio (KR), magnesium adsorption ratio (MAR) and residual sodium carbonate (RSC). It was observed that some quality deterioration occurred locally and was in line with the discharge of urban and industrial wastewater into the basin. The type was predominant and the hydrochemical facies was characterized by carbonate weathering with minor evaporite effects.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".