Communicating Flooding Issues to the Public at Large
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it