The apple snail, Pomacea canaliculata: an evidence note on invasiveness and potential economic impacts for East Africa
Bibliographic record
Abstract
The South American freshwater apple snail, Pomacea canaliculata (Lamarck) has been introduced and become invasive in many parts of the world, causing significant economic losses in wetland rice cultivation, threatening biodiversity and impacting on human health.The confirmed report in 2020 of this snail species damaging rice crops in Mwea, the most important irrigation scheme in Kenya, represents a new introduction to continental Africa and brings into focus the need for a rapid and coordinated response to contain and mitigate the risk to other rice schemes, as well as neighbouring countries.This evidence note provides a review of the global invasive spread of P. canaliculata, its biology and ecological adaptability and assesses the risks and potential economic/yield losses for sub-Saharan African rice production over the next decade under different control scenarios.Given the strategic importance of rice production in the region, the potential impacts on food security and farmer income could be considerable.Monitoring, sustainable management and good agricultural practice recommendations are synthesized from established global resources and information is collated to support preparatory action and rapid response.Given the relatively localized distribution of the snail in Kenya, the implementation of a coordinated snail containment and eradication plan is urgently called for, underpinned by countrywide awareness raising, education and outreach to facilitate community-based vigilance and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".