Pathways of change in the socio-shysical dynamics of the Western Indian Ocean deltas
Bibliographic record
Abstract
We present the output of a research combining field based, expert knowledge and remote sensing, based on Google Earth Engine, aimed at the identification of the rates of changes and pathways during the past 35 years, in four Western Indian Ocean River Catchments and Deltas: Tana River in Kenya, Rufiji River in Tanzania, Limpopo River in Mozambique and Betsiboka River in Madagascar. These findings are a set of preliminary results of the collaborative and multidisciplinary effort produced within the GDRI-Sud network DELTAS and as a follow-up of the West Indian Ocean Deltas Exchange and Research network (WIODER) project that brought together the National Museum of Kenya, , University of Dar Es Salaam in Tanzania, University Eduardo Mondlane in Mozambique, Centre National de Recherches sur l'Environnement in Madagascar, University of Southampton in UK, IHE Delft in the Netherlands, Institut de Recherche pour le Développement in France, and International Development Research Centre in Canada and Kenya. We highlight the similarities in the physical environment and, where possible, also in the socio-economic-political environments that are leading the current changes, potentially affecting resilience of the local population and their sustainable development. We focused on the substantial changes in the following aspects: precipitation seasonality, flooding patterns and frequency, land cover, dry forest cover, mangrove cover, crop production, fish population, human population, human migration flow, frequency of human conflicts within the delta population. The observed changes call for reflection given the IPCC projections in climate towards an aridification of the Southern Africa river basins and a wetter condition in the Eastern Africa region. Some signals of these climatic forecast are already recorded in both regions and will be explored in the DIDEM project.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".