Impact of Inadequate Urban Planning on Municipal Solid Waste Management in the Niger Delta Region of Nigeria
Bibliographic record
Abstract
This study examines the impact of inadequate urban planning on municipal solid waste management (MSWM) in the Niger Delta Region (NDR) of Nigeria. The continuous migration and high concentration of people, administration and industrial activities in the region with little or no implementation of urban planning procedures during the development of the settlements in the region has contributed to increase the problem of MSWM in the NDR. It is not uncommon to see streets, roads, undeveloped plots of land and drains littered with solid waste in most Niger Delta cities, towns and communities. The data for this research were gathered from field surveys, observations, questionnaires as well as desktop information from published materials. Chi-Square statistical method was used in the analysis of the correlation data. The results show that there is a strong relationship between inadequate waste collection and the existence of unplanned settlements in the region. The study also revealed that indiscriminate waste disposal is strongly linked with the existence of unplanned settlements in the NDR of Nigeria. Therefore, the implementation of urban planning procedures and inclusion of waste management during the building and development of cities, towns and villages in the Niger Delta should be taking as a matter of high priority if cities in the region are to be clean and free from wastes and environmental pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".