Assessing the influence of seasonal precipitation patterns on groundwater quality in the coal rich environment of Enugu, Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
Abstract As the impacts of climate change continue to be felt around the world, understanding the effects on groundwater quality and quantity has become an important area of investigation. As a global source of water that contributes to preserving the environment, a better understanding of the effect of precipitation seasonal pattern on these systems is crucial; though studies connected to groundwater quality in this era of environmental crisis are at infancy. This study aims to evaluate the effect of precipitation seasonal pattern on groundwater quality in a coal enriched environment of developing city of west African sub-region with particular reference to Enugu, a coal city in Nigeria. Three residential areas (Abakpa, Achara, and Independence Layout) were randomly selected from high, medium, and low neighbourhood densities in the metropolis. Within the period spanning from April 2018 to March 2019, a physiochemical analysis was conducted on twelve deep wells utilizing weighted arithmetic index method. This technique was implemented in order to facilitate the assessment of the degree of water quality by translating a number of variables to just one metric value. The results of the investigation showed that the groundwater resources in the study region are mildly acidic, presumably as a result of the presence of pyrite, which is a byproduct of coal weathering, and chloride-ion-charged rains. Additionally, noticeable distinctions in the properties of water samples were observed between the dry and rainy seasons. Specifically, just 1% of the sampled water had excellent ratings, while 58.3% were considered good, 29.1% were deemed poor, and 8.3% of samples were categorized as very poor. The study concluded that coupled with climate crisis, seasonal precipitation patterns affect groundwater resources by reducing recharge, discharge, and the overall quality of water. These results have important implications for the management of groundwater resources in the region and highlight the need for continued monitoring and assessment of water quality in the face of ongoing environmental changes.
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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) | 0.000 | 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 it