Communicating Flooding Issues to the Public at Large
Bibliographic record
Abstract
The coastal area ofKwaZuluNatal is subject to regular flooding, ranging from severe regional flood events such as the 1987 floods when 300 people lost their lives to localised flash floods causing erosion damage.The city of Durban is particularly vulnerable to flood-related problems due to the large urban population and limited amount of developable land.In the past, residential and commercial/industrial development has taken place in flood prone areas placing lives and property at risk.The National Water Act of 1998 states that information relating to floods and potential risks must be made available to the public.The challenge for Durban, with hundreds ofkilometres of rivers located in the municipal area, has been to develop a programme to gather flood-related information, identify the parties to whom it should be distributed, and distribute the information in an efficient and appropriate manner.The use of geographical information systems (GIS) has enabled flood studies to be carried out quickly andinauniformmanner, with results beingloadeddirectlyinto the Municipality's GIS database.By storing the flood-related information in the GIS environment, it is available to other departments within the Municipality and also the general public via the eThekwini website.The main users of the information in the Municipality are the City Engineering Unit, Disaster Management Department and Development and Planning Department.In addition to the internet, the information is disseminated to the public through direct mailing and word based community Disaster Management Committees.Future developments will include devising better and more efficient ways of informing those living in McDonald, A., A.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".