Ground Water Conditions and Spatial Distribution of Lead and Cadmium in the Shallow Aquifer at Effurun- Warri Metropolis, Nigeria
Bibliographic record
Abstract
A water table head distribution map of the shallow Benin Formation aquifer in the Effurun-Warri area has been drawn from dug well data and used to define groundwater gradients as well as identify directions of groundwater movement in this densely populated urban setting. Water samples from forty dug wells were also screened for the presence of lead and cadmium and results showed a variation in concentration from not detectable to 0.04mg/l for each metal. Iso-concentration contours for lead in groundwater suggest that enrichment may be from two sources: wastes from the refinery and petrochemical industrial complex on the northwestern edge of the city and secondly from leachates associated with the many unregulated waste dumpsites. Lead appears to be constrained from spreading eastwards from the industrial complex area by the south and westwards trending groundwater gradient. The city wide prevalence of elevated levels of cadmium is also probably due to leachates from unregulated dumpsites as well as the mixing of groundwater as suggested by existing gradients. Potential implications of the findings for public health, local and regional water quality monitoring are discussed.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".