Post-War Waste Composition: Household Waste Management in Misrata City, Libya
Bibliographic record
Abstract
Waste generation and its composition reflect activities of a society, due to the fact that it is driven by socio-economic interactions, political structure, and social security. However, changes to waste composition may go unnoticed, except where a database on household solid waste (HSW) is available. Libyan cities were devastated by the scourge of war due to the “Arab spring”. This necessitates planning and development to tackle waste management. This study aims to determine the composition of household solid waste in Misrata, Libya, to generate waste stream data that eludes most post-war cities in North Africa and Middle East, which can be used to plan and subsequently manage waste collection services, treatment options, and disposal methods. Discrete classification and direct measurement of HSW from selected families (30) in Misrata were utilized to assess waste composition and changes across households. 400 questionnaires were distributed to residents to determine public perception and its correlation to waste composition. The results confirmed that the highest amount of HSW generated was organic waste, which accounts for 52 %, followed by 20.7%, 16% and 5.9% generated from miscellaneous waste, plastics, and paper wastes, respectively. Metals and glass reported the lowest HSW components, at 3.9% and 1.5%, respectively. The survey component of the study indicated that more than 70% of the respondents claimed that recyclable items are increasing, especially plastics, due to changes in life style and income.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".