Reduction of Occupational Risks at Low-Tech Composting Plants in Developing Countries - Case Study ENPRO Composting Site LomÉ, Togo
Bibliographic record
Abstract
Manual sorting and composting of mixed municipal household waste is associated with occupational hazards. This case study aimed at finding sustainable solutions to alleviate occupational risks for workers at a composting site in Lomé, Togo and at demonstrating a pragmatic way to apply international safety standards to composting facilities in developing countries.Occupational risks were assessed by means of onsite data collection and workshops and interviews with workers and management representatives. The data were the basis for the evaluation of the risk as a function of “Severity of the potential harm” and “likeliness that harm is done”. Mixed household waste is delivered to the composting plant. Pre-sorting and sorting non-decomposable residuals out is time consuming and requires a large number of workers. During all compost production procedures, workers are exposed to pathogen containing dust, experience musculoskeletal burdens due to handling heavy loads and face the risk of cuts caused by sharp items. Adverse environmental conditions increase occupational risks. Delivery of the organic waste fraction separated at household level is recommended toomit risky work steps and to increase process efficiency. For the current mode of operation, we present process re-engineering options and organisational measures to reduce occupational risks and we discuss implementation constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".