The Asian Citrus Greening Disease (Huanglongbing): Evidence Note on Invasiveness and Potential Economic Impacts for East Africa
Bibliographic record
Abstract
Huanglongbing (HLB), also known as citrus greening disease, is one of the most devastating pathogens of citrus worldwide and is caused by closely-related species of systemic Candidatus bacteria.Vectored by the Asian citrus psyllid (ACP), Diaphorina citri, the heat-tolerant, Asian form of the disease, Candidatus Liberibacter asiaticus (CLas) is the most serious and widespread.In East Africa, the heatsensitive form of the disease is vectored by the African citrus triozid (ACT) Trioza erytreae and remains the most prevalent disease, particularly in the cooler, mid to high altitude areas.Since 2010 however, CLas which can also be transmitted by T. erytreae, has been spreading in the African continent and significantly, the Asian HLB vector has also been detected in Tanzania and Kenya.There is now a clear and present threat of HLB to the citrus industry in Africa, including the previously sustainable warmer citrus producing regions of East Africa.Modelling of environmental suitability suggests that without preventative measures CLas could establish widely in Africa, with potential hotspots in Central and South-eastern Africa, incurring substantial economic losses.The following evidence note reviews the global literature on the HLB pathogen-vector complex, highlighting potential risks and estimating economic impact for Africa.A synthesis of recommendations for biosecurity preparedness, surveillance and management options is outlined to inform decision makers and growers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".