Reducing Urban Litter in South Africa through Catchment Based Litter Management Plans
Bibliographic record
Abstract
South Afiica generates in excess of 40 million tonnes of solid waste eve1y year -mostly of domestic origin. More than 780 000 tonnes of this is washed into the drainage system where it ends up entangled amongst the vegetation and sediments along the banks of the streams, rivers and lakes or strevv11 on the beaches. To remove all the litter tiom the watercourses without seeking to reduce the quantities involved would cost South Africa at least US$400 million per annum-or approximately 0.4% of its gross domestic product (GDP). This is clearly not feasible and therefore the Water Research Commission of South Africa and the Cape Metropolitan Council are funding a four year investigation into the reduction of urban litter in the drainage systems through the development of catchment specific litter management plans. Eight storm water drainage catchments, representing a diversity of land-uses, have been selected for a detailed litter audit. This audit will quantify the amount and type oflitter being deposited in the drainage catchments both before and after the implementation of various litter management strategies. The results of the litter audits will measure the effectiveness of the various litter management strategies, which in tum will facilitate the continuous improvement of the litter management plans.
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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 | 0.002 | 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 | 0.001 |
| 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".