Vulnerability to climate change in three hot spots in Africa and Asia: key issues for policy-relevant adaptation and resilience-building research
Bibliographic record
Abstract
Providing sound evidence to inform decision-making that considers the needs of the most vulnerable to climate change will help both adaptation and development efforts. Such evidence is particularly important in climate change “hot spots”, where strong climate signal and high concentrations of vulnerable people are present. These hot spots include semiarid regions and deltas of Africa and Asia, and glacier- and snowpack-dependent river basins of South Asia. In advance of a major research effort focusing on these three hot spots, studies were commissioned to identify and characterize the current status of knowledge in each on biophysical impacts, social vulnerability, and adaptation policy and practice. The resulting seven papers are brought together in this special edition, with this editorial introduction providing background on these hot spots, the program through which the studies were commissioned, and an overview of the papers that follow.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| 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".