Problems Encountered with the Measurement of Urban Litter entering the Stormwater Systems of Cape Town
Bibliographic record
Abstract
There is a paucity of data on the nature and quantity of urban litter (alternatively called trash or rubbish) that finds its way into the stonnwater drainage systems (Armitage et al, 1998;Armitage & Rooseboom, 2000).This chapter relates how a data collection process was instituted in nine small (34-144 ha) catchments in the City of Cape Town under the Water Research Commission Project No. KS/1 051 entitled The reduction of urban litter in drainage systems through integrated catchment management.In keeping with the aim of improving the knowledge of the source, type and amount of urban litter coming from different types of urban catchments, these catchments covered a range of different land uses, socio-economic levels and densities.The study catchments, the simple devices used to trap the litter, the steps taken to implement the process, including the installation of the trapping devices, and the monitoring procedure are described.The constraints experienced, both in setting up the catchments to enable the collection of the data, and in recording the data are discussed.It is hoped that the lessons learned will prove useful to researchers tackling similar investigations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".