The applications of Canadian water quality index for ground and surface water quality assessments of Chilanchil Abay watershed: The case of Bahir Dar city waste disposal site
Bibliographic record
Abstract
Abstract Surface water and groundwater have been experiencing increasing risks of contamination in recent years because of the poor management of the immense amounts of waste created by different human activities. Inappropriate dump sites have served for many years as marginal disposal sites for a wide range of wastes, including solid waste, fresh sewage and hazardous waste, in developing nations such as Ethiopia. Physical, anthropogenic and organic procedures continuously interact to deteriorate the waste. One of the results of these practices is artificially contaminated leachate, which is potentially hazardous waste from disposal sites. If not managed appropriately, such a dumping site can contaminate groundwater (through leachates) and surface water (through contaminant transport by flooding and groundwater movement). Along these lines, this study focuses on the applications of water quality index in the ground and surface water quality caused by the waste disposal site of Bahir Dar city within the Chilanchil Abay during the study period. Water testing was performed on five samples of surface water and six samples of groundwater in each month from 30th March (dry season) to 20th August (wet season). More than 13 water quality parameters, for example, pH, TDS, electrical conductivity, turbidity, temperature, dissolved oxygen (DO), TH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), TC, NO3−, PO43−, Cr, Mn, and Pb contents, were examined in both ground and surface water. It was discovered that water quality status of the Chilanchil Abay watershed ranges from 15.87 to 36.6 for surface water and 42 to 46.2 for groundwater suggesting poor and marginal status for drinking water purpose.
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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 | 0.002 | 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".