A research framework to assess the impacts of oil pollution on Niger Delta ecosystems
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».