A research framework to assess the impacts of oil pollution on Niger Delta ecosystems
Bibliographic record
Abstract
Nigeria's oil-rich Niger Delta region is the country's lifeblood, contributing 85% of the national economy. Paradoxically, the region remains heavily dependent on fishing and farming as essential means of survival. However, its entire ecosystem, which supports these services, is under severe threat due to relentless crude oil exploration. This study uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic literature review protocol to design a framework. This framework depicts the various impacts of oil spills on the Niger Delta ecosystems, particularly forest ecosystems, rural livelihoods and food security. The review process was completed in four stages: data acquisition, document screening, qualitative data extraction, and results presentation. First, data acquisition involved searching relevant articles from various sources, including Google Scholar, Web of Science, Elsevier Scopus, PubMed Central and relevant grey literature. Exclusion criteria were used to remove documents that were not relevant for this research. The included article used a reproducible method, reported relevant outcomes, and focused on oil pollution in the Niger Delta of Nigeria. One hundred and fifteen (115) full-text publications were eventually obtained for data analysis using NVIVO software. The result analysis stage involved the coding, identification, and interpretation of themes obtained by querying the compendium of information obtained. Three layers that bear different components and subcomponents that are affected by crude oil were identified. The first layer (Layer 1) consists of humans, Aquatic Environment, and Terrestrial Environment. The authors discussed the impacts of oil pollution on the Humans component, with a percentage probability of 78% greater than both Aquatic Environment and Terrestrial Environment components. Layer 2 consists of six factors that are affected by oil spills. Of these factors, Human Health and Mortality Rate and Forest Habitat and Ecosystem Services share equal significance. However, the impact on Water was scarcely discussed by authors, as it accounts for just 39.1% probability. Layer 3 consists of sixteen factors (16). Some of the most discussed factors by authors are Aquatic Vertebrates, Vegetation, and Consumption patterns. In contrast, some of the least discussed factors are Human Displacement, Conflicts, Aquatic Invertebrates, Human Exposure, and Food Quality.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".