Toward Locust Management: Challenges and Technological opportunities, Sikaunzwe, Zambia
Bibliographic record
Abstract
Locust invasions have proved to be a threat to the world’s food security and livelihood. Governments in locust infested areas in Africa have adopted various early warning strategies aimed at preventing and eliminating the impact of both African Migratory Locusts and Red Locusts. These measures include community sensitisation, use of eLocust3 early warning system and spraying of affected areas using recommended pesticides. Management of locusts in the study area, Sikaunzwe Agriculture Camp in Zambia, is however faced with unique challenges. The research was focused on exploring challenges faced by the ministry of agriculture in managing the spread of locust invasions using the existing early warning strategies. Focus Group Discussion (FGD) method was used in the study and NVivo 11, a qualitative data analysis software, was used to analyse the data based on thematic coding framework. The following challenges were acknowledged; failure to identify correct locust species, limited field staff and inaccessibility of infested areas. The proposed technology solutions to the above challenges include the use of machine learning, low cost drones, geospatial technology and Internet of Things.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".