Exploring the Influence of the Interaction of Climate Change, Manmade Threats and COVID-19 on the Livelihoods of Wetland Communities in Sub-Saharan Africa
Bibliographic record
Abstract
Wetlands are very important because of the wide range of ecosystem services they provide. Despite their ecological, social and environmental importance, these ecosystems are threatened and fragmented under the combined effects of climate change (CC) and man-made activities (MMA). Such a state of things could be exacerbated by the advent of the coronavirus disease 2019 (COVID-19) pandemic with its many implications. In order to help decision-makers take good decisions, the combined effect of CC, MMA and COVID-19 on the livelihoods of communities around wetland ecosystems have been reviewed based on available scientific knowledge. First, we analyzed the different concepts and theories underlying the wetlands-related studies and then summarized the merits and demerits of the different methodologies underlying wetland studies. The empirical evidences that exist in previous literatures have been highlighted. Similarly, common livelihood strategies for wetland communities in Sub-Saharan Africa (SSA) have been highlighted. The diversity of wetlands’ functions and services makes them a source of livelihood, food security and poverty alleviation for riverside communities. However, these communities lack the knowledge and awareness to understand the impact of their activities and CC on their livelihoods. The review also helped to identify that, out of the three factors investigated, the livelihoods of rural wetland dwellers in SSA are mostly influenced by CC and MMA. However, climate change and COVID-19 remain life-altering transboundary threats that extend in space and time, with large uncertainties on wetlands communities livelihoods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".