Seasonal Variation in Water Quality of Lukha River, Meghalaya, India
Bibliographic record
Abstract
Lukha River (Wah Lukha) is one of the major rivers of Meghalaya situated in the southern part of East Jaintia Hills District. Activities such as mining of coal and limestone, manufacturing of cement, deforestation etc. have been taking place in the catchment area of the river leading to changes in water quality. This is evident from the deep blue appearance of water of Lukha River during winter months for the last 7-8 years.Till date no convincing and conclusive reason has been given for this annual change in physical appearance.To get insight, we studied the physico-chemical water quality parameters of this river in different seasons and found that the water quality has started deteriorating due to activities occurring in the catchment area. Based on Canadian Council of Ministers of the Environment-Water Quality Index (CCME-WQI) the water of the river at some locations was found of ‘poor’ quality.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.011 | 0.002 |
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; both teacher heads agree on what is shown here.
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".