CSIR launches novel online climate risk profiling and adaptation tool: The Green Book
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 Council for Scientific and Industrial Research (CSIR) has recently launched a state-of-the-art online climate risk profiling and adaptation tool to assist municipalities across South Africa to assess their risk and vulnerabilities, and respond by adapting settlements to climate change. The Green Book looks forwards to the year 2050 by projecting settlement growth combined with quantitative, scientific evidence of the likely impacts that climate change will have on South African towns and cities and its key resources. The tool provides appropriate adaptation measures to be considered for implementation towards the development of climate resilient settlements. The ultimate goal of the Green Book is to contribute to resilient, sustainable and liveable South African settlements through climate change adaptation. Co-funded by the Canadian International Development Research Centre and the CSIR and produced in collaboration with South Africa’s National Disaster Management Centre, the Green Book is the result of a 3-year initiative. More than 50 researchers and numerous stakeholders and reviewers were involved in producing the Green Book and reviewing its findings.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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