A Geospatial Appraisal of Oil Spill Health Impacts: A Niger Delta Case Study
Bibliographic record
Abstract
Oil spills resulting from pipeline breakages and operational failures during oil exploration have increased in prevalence in the Niger Delta, with more than 8,000 spills occurring over the past decade. Previous research has linked oil spills to human health hazards such as derma-toxic diseases, and various cancers. However, few studies have considered the health effects of oil spills on maternal and child health. This study seeks to fill this gap in literature by focusing on infant diarrhea in the Niger Delta. Diarrheal diseases account for 1 in 9 child deaths worldwide, making diarrhea the second leading cause of death among children under the age of five. Defining the spatio-temporal pattern of infant diarrhea and its relationship to oil spill contamination is critical in understanding mortality risk and enabling policy decisions aimed at reducing infant mortality rates in the region. Despite substantial data limitations, geospatial analysis revealed a statically significant spatial clustering of oil spill incidence and infant diarrhea: a pattern strongly correlated with spatial proximity to rural, low-income households with limited access to improved water sources and sanitation facilities. These locations evidenced higher rates of diarrhea incidence consistently across the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".