Evaluation of spatio-temporal water quality status of Jeera river, Odisha, India
Bibliographic record
Abstract
Jeera River of Bargarh District, Odisha faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The river was formerly a flourishing tributary of the massive Mahanadi River that possessed excellent navigability, an array of aquatic ecosystems, and a well-established basin with an expanding agricultural sector. The current condition of the Jeera River is deplorable, leaving behind only minimal economic and ecological values. The study emphasizes analyzing the seasonal variation of the water quality rating of Jeera River in terms of the Water Quality Index (WQI). WAWQI (Weighed Arithmetic Water Quality Index) values show that almost all sampling sites have poor or unsuitable quality. During the monsoon season, the water quality deteriorated the most, with an average WQI score of 516.430 compared to pre- and post-monsoon with average WQI values of 154.558 and 276.014 respectively. CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) values indicate that water quality ranges from marginal, and poor to fair. This study concludes that out of the eight sampling sites, station 5 (Dumerpali) is observed to be the most polluted site. Many water quality parameters including iron, turbidity, nitrate, phosphate, E. coli, and Total coliform are found to exceed the permissible limits prescribed by WHO and BIS. Reducing sewage outflow, blocking direct stormwater discharge, and avoiding continuous solid garbage disposal by neighbouring populations are ways to improve river water quality.
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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 | 0.001 |
| 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 | 0.000 |
| 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".