Agricultural innovation and environmental change on the floodplains of the Congo River
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate‐driven environmental changes bring new risks but also opportunities to populations living along the world's major rivers. Based on ethnoecological fieldwork, in this paper we examine how people living in the cuvette centrale of the Congo basin have adopted flood‐recession agriculture on islands in the Congo River, taking advantage of a secular shift since the 1980s in the hydrological regime of the Congo River. Analyses of the hydrological data reveal that this shift decreased flood risk and significantly extended the growing season on the islands, long enough to enable cultivation of fast‐maturing varieties of manioc and other crops. Flood‐recession farming on islands in the river is today not only an important source of food, but also a source of income for women, who are primarily responsible for seasonal cultivation of fields during the low‐water season. Hydrological changes alone are insufficient to explain the adoption of the new agricultural practice; adoption also arose as a result of dynamic interactions among river fishing, trading, and broader socio‐economic forces. Climate‐change models project an increased frequency of extreme floods. Our results suggest that this change may limit island cultivation in the future. More generally, our findings point to the importance of looking beyond single‐factor, solely environmental explanations in studies of climate‐change adaptation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it