Impact of the 2012 Flood on Water Quality and Rural Livelihood in the Orashi Province of the Niger Delta, Nigeria
Bibliographic record
Abstract
Flood event is not considered to be a natural hazard unless there is a threat to human life and/or property as the case of the 2012 flood incident in most parts of the Niger Delta, Nigeria. Thus, this paper aims at assessing the impact of the flood on groundwater quality of the affected areas, as well as the livelihood of the affected rural people. With a focus on six communities in the ORASHI province of the Niger Delta, which is one of the worst hit by the flood, the authors generated and analysed data that was used to draw conclusion for this study. A total of 2 water boreholes and 3 open artisan wells in each of the six (6) communities were sampled. These samples were subjected to both physico-chemical and microbial analysis against WHO standards. The result shows that the various water samples came short of the WHO Standards for safe water. Thus it could be deduced that the 2012 flood triggered damages not only to the life of individuals, properties/ infrastructure, but also most of the drinking water sources, especially, streams and the dug-out wells which were submerged in the event of the flood. The paper discovered that the flood incident seriously devastated the rural economy; farming, the major source of livelihood. The flood has made livelihood support difficult for the people of the area. Thus, community initiated mitigation measures should be promoted so as to strengthen community resilience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".