Examining the Effects of Internal Armed Conflict on the Nigerian Environment and the Response of Government
Bibliographic record
Abstract
The incidences of armed conflicts in the North-East of Nigeria and the Niger Delta have left a lot of devastation on the environment and the livelihood of civilians. The paper notes that the Niger Delta conflict resulted in bombing of pipelines and oil facilities that led to discharges into freshwater sources and the farmlands causing a devastation to the environment and threatening human lives due to excessive amounts of toxic materials being discharged. Constant gas flaring affects wildlife and human life negatively. Badly constructed canals and causeways for the purpose of mining activities have adversely affected the hydrology of the region, causing floods in some areas and water scarcity in others. These artificial waterways allow saline water leakage into the sources of freshwater, resulting in scarcity of drinking water and the mortality of many aquatic plants and animals. When petroleum is discharged into the soil, the soil becomes acidic, which disrupts photosynthesis and respiration of tree roots. The paper, therefore, recommends that the government must address the root causes of conflict and undertake environmental clean-up seriously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".