The Characteristics of Retail Wastes in the City of Yenagoa, Nigeria
Bibliographic record
Abstract
<p class="EbiBody"><strong>Wastes management is a clear strategy where wastes are channelled through processes that ensures proper storage, collection, transportation, treatment and disposal of wastes with ample consideration for environmental protection and public health. In Yenagoa where the current study was conducted, the lack of proper wastes management structure is apparent, particularly among retailers where samples were drawn from for the study. Little is known on the volume and characteristics of wastes produced by the retail sector in the city, like many other Nigerian cities.</strong></p><p class="EbiBody">The study employed a mixed method approach using closed and open-ended questionnaires in collecting data. In all about 900 questionnaires were collected and analysed for the study. Knowing that no proper waste management plan can be developed until the current practice, sources, components and volume of waste has been well understood. The use of qualitative methods in this paper helps to better understand and gather data on areas of interest. A multi stage cluster sampling technique was employed due to the unavailability of an up-to-date sampling frame in the area.</p><p class="EbiBody">The findings from the study show that wastes materials produced by retailers in Yenagoa are mainly wastes paper, tins, cans, plastics, cardboard, furniture, wood products and possibly WEEE. However, plastics and cardboard materials were the dominant wastes materials produced by the retail sector. The study therefore concludes that there is a need to establish a wastes processing facility in Yenagoa with heavy emphasis on recycling.</p>
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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 | 0.004 | 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 | 0.001 | 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".