Assessment of impact of leachate on hydrogeological repositories in Uyo, Southern Nigeria
Bibliographic record
Abstract
Electrical resistivity methods integrated with physico-chemical methods of water analysis were employed to assess the impact of dumpsite leachate on groundwater repositories in Uyo, Nigeria. The uneven distribution of the bulk resistivity, which range from 82·3 to 2705 Ω m, indicates the ingress of leachate from degraded materials into aquifer units for depths ranging from 15·8 to 60·1 m. The low longitudinal conductance of <0·5 Ω−1 indicates the susceptibility and vulnerability of the hydrogeological units to the leachate emanating from the dumpsite. The seemingly high bulk conductivity values also show the presence of organic contaminants in the arenaceous materials expected to have been characterised by freshwater. The results of the vertical electrical sounding (VES) and electrical resistivity tomography showed low resistivity to the depth extent of about 15 m, except in VES points away from the dumpsite. The analysis of water samples revealed a slightly acidic groundwater, which, on the average, falls below the World Health Organization (WHO) standard. The chemical parameters compared with the WHO standard were below the WHO standard for drinking water, and this suggests an interaction between the leachate and the geofluid. The sodium adsorption ratio, the magnesium adsorption ratio and Kelly’s ratio were also calculated in order to determine the suitability of the groundwater for agriculture (irrigation).
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".