<i>Mimosa pigra</i> in eastern and southern Africa: Distribution and socio‐ecological impacts
Bibliographic record
Abstract
Abstract The semiaquatic weed Mimosa pigra has negative impacts on biodiversity, fishing, crop and livestock production, and tourism in most places where it has been introduced, established and proliferated. Many of the ecological impacts are well known, but its impacts on rural livelihoods are less well documented, especially in Africa. We mapped the distribution of M. pigra in eastern and southern Africa, and then compared that with its potential distribution based on an ecoclimatic niche model. Household interviews were conducted to assess the impacts of this weed on local livelihoods. Mimosa pigra was found to be invasive in western Ethiopia, around the shores of Lake Victoria and Lake Tanganyika, and along the Tanzanian coastline, northern Malawi, parts of Mozambique and along the Kafue River and in the Barotse floodplain on the Zambezi River in Zambia. According to respondents living along the Kafue River floodplains in Zambia, it has a negative impact on biodiversity, wildlife, livestock, crop production, fishing and mobility. Dense stands prevented the movement of people and livestock, limiting access to croplands, grazing lands and fishing areas. Fish catches have been reduced and fishing equipment damaged. All respondents agreed that their livelihood options would be considerably enhanced if M. pigra was removed from the landscape. Based on its current and potential impact, we therefore recommend that an integrated management plan be developed and implemented, including the appropriate use of biological control agents to reduce the negative impacts of the weed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".