GROUNDWATER QUALITY ASSESSMENT OF CHILANCHIL ABAY WATERSHED: THE CASE OF BAHIR-DAR CITY WASTE DISPOSAL SITE
Bibliographic record
Abstract
Improper dump sites have served many years as an ultimate disposal site for all types of waste; municipal solid waste, industrial sewage and hazardous waste in developing countries such as Ethiopia. Physical, chemical and biological processes interact simultaneously to bring about the overall decomposition of the wastes (chemically laden leachate). If not, deal properly, such kind of dumping site can cause pollution to groundwater (because of Leachates) and surface water (through contaminant transportation by flooding and groundwater movements). Therefore, this study investigates the spatial and temporal variation of groundwater quality within the Chilanchil Abay watershed during dry and wet season due to the waste disposal site of the Bahir Dar city. Water samples were collected from 6 sampling points of groundwater from 30th March to 20th August by a monthly period. Over 10 water quality parameters such as pH, TDS, Electrical conductivity, Turbidity, Temperature, DO, BOD, COD, TC, NO3- and PO43–, were analyzed. Moreover, the overall status of the groundwater quality of the study area was evaluated by the Canadian Water Quality Index. Based on the result of this model the status of the groundwater sample points throughout the study area were raged from 42 to 46.2 (marginal status).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".