A review of the biophysical impacts of climate change in three hotspot regions in Africa and Asia
Bibliographic record
Abstract
Abstract A systematic review was conducted of biological and physical climate change impacts in three hotspot regions in Africa and Asia. Specifically, the review focused on identifying the nature and extent of biophysical impacts in semi-arid zones, mega-deltas and glacial-fed river basins. In total 139, peer-reviewed articles were reviewed, with a steady increase in relevant articles reported since 2006. Publications on the South Asian glacial-fed river basins were the most numerous followed by semi-arid areas and then deltas, with Central Asia and some African countries being the most under represented. The nature and extent of impacts varied for each hotspot area and were largely determined by the geographical context and intrinsic characteristics of each region. River basin publications were dominated by impacts concerning hydrology, highlighting the importance of glacial-fed water resources to downstream populations. Semi-arid regions were dominated by impacts to climate processes and impacts to livestock and vegetation highlighting the importance of rainfall to the ecosystems and the livelihoods of communities in these regions. In contrast, delta studies were dominated by a focus on hazards, predominantly coastal inundation, reflecting the concentration of populations and assets in these areas. Uncertainties associated with the biophysical impacts on these regions under a changing climate are documented and represent key knowledge gaps. Common information gaps for all hotspot regions were the need for improved hydro-meteorological monitoring systems. The development of climate change adaptation strategies and policies should be supported by a sound knowledge and understanding of the full range of biophysical impacts, which are characteristic to each geographical location.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".