Municipal solid waste management in tarkwa area council (TAC), ghana
Bibliographic record
Abstract
Effective municipal solid waste (MSW) management rids a community of fi lth and makes it healthy for human habitation.However, it is notably missing in several parts of Ghana as evidenced by low coverage and irregular waste collection services and an unconcerned public attitude towards waste disposal.The situation is exemplifi ed in the Tarkwa Area Council (TAC), a mining community in Ghana.Lack of data on waste generation, storage, transfer and transportation and disposal in the area makes MSW management very diffi cult.The objectives of this research, therefore, are to investigate how MSW is managed in TAC, identify short falls and make appropriate recommendations for improvement.Questionnaires were given to 420 residents in the municipality focusing on their background, major environmental concerns, current waste disposal practices and their perspectives of general waste management.Air pollution, inadequate waste collection and unsafe waste disposal were the major environmental concerns of the residents.Food remains, sweepings and plastics are the major types of waste produced.About 45.5% of residents were not satisfi ed with the level of MSW management in TAC.Waste is not separated at source and hence, not subjected to recycling.The fi nal waste disposal site is not engineered and is operated as a dump.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".