The Use of GIS to Determine a Strategy for the Removal of Urban Litter Upper Lotus and Lower Salt River Catchments, Cape Town
Bibliographic record
Abstract
Urban litter pollution is a persistent problem in rivers, canals and drainage pipelines throughout South Africa.Part of an integrated approach to achieve a reduction oflitterpollution in the storm water systems involves the development of optimized strategies for the removal of litter from the pipelines and canals.This can only be achieved by developing a clearer understanding of the volume, source and distribution of litter within South African stormwater systems.This chapter presents a study in which a litter generation model was developed to estimate the source and quantities of litter in the upper reaches of the Lotus River and lower reaches of the Salt River catchments.Using GIS modeling techniques, the litter generation model integrated land-use data sets with litter wash-off rates for various land-use types to determine the quantity of litter generated from discrete sub-catchments within the study area.This model was then used as an aid in selecting the most appropriate litter removal devices in optimal locations within the study area to achieve the greatest litter removal at the lowest cost.A practical phased implementation program was thus developed for the City of Cape Town that could potentially remove 65% of the litter from the rivers and canals in the study area.Wise, C. and N. Armitage.2004."The Use of GIS to Determine a Strategy for the Removal of Urban Litter Upper Lotus and Lower Salt River Catchments, Cape Town."Journal of Water Management Modeling R220-24.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".