The Institutional Challenges and Opportunities for Adopting Landscape-Based Storm Water Management Options in Informal Settlements - Dar Es Salaam City
Bibliographic record
Abstract
Increased flooding caused by climate change impacts is a challenge for many cities both in developing and developed countries. The existing storm water drainage systems in place have to be physically constructed and expanded to meet the water run-off challenge. This is an expensive run-off management undertaking for resource poor countries such as Tanzania. Landscape based storm water management (LSM) is put forward as a sustainable option to manage storm water run-off and it also addresses water scarcity problems in under-served urban settlement. However its implementation in cities that are faced with informal residential development is challenging because among other things, LSM requires land for implementation as well as the collaboration of different institutions, disciplines and actors. Drawing from data and information obtained from the Water Resilient Green Cities Africa (WGA) Project in two cities of Africa, this paper explores the planning and institutional challenges for LSM in Dar es Salaam, a rapidly urbanising city. The paper also presents opportunities inherent in the process some of which suggest that local institutions offer a critical platform to collaboratively plan and implement LSM in rapidly urbanising cities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".