Mapping Multiple Climate-related Hazards in South Asia
Bibliographic record
Abstract
This Research Report presents the first comprehensive overview of the multiple climate hazard risks, and the proposed key issues and challenges facing the South Asian region. This report suggests methods for mapping such risks and estimating their impacts on people and agriculture in South Asia. Regional, country-wise and sub-national assessment of five climate-related risks – floods, droughts, extreme rainfall, extreme temperature and sea-level rise – is carried out. The approach involves overlaying climate hazard, sensitivity and adaptive capacity maps, and follows the vulnerability assessment framework of the Intergovernmental Panel on Climate Change (IPCC). A combined index based on hazard, exposure and adaptive capacity is introduced to identify areas susceptible to extreme risk. There is a lack of a systematic and comprehensive risk assessment capturing multiple climate hazards for the entire South Asian region and the need for a common framework for risk assessment. While this approach is well grounded in theories and integration of various spatial data including remote sensing data to derive hazard information, there is a clear need for linking additional elements from the ground at a finer scale among various sectors in developing comprehensive risk assessment information for a disaster risk management plan and promoting risk financing strategies.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".