The Use of Radar Remote Sensing for Identifying Environmental Factors Associated with Malaria Risk in Coastal Kenya
Bibliographic record
Abstract
Malaria remains one of the greatest killers of human beings, particularly in the developing world. The World Health Organization has estimated that over one million cases of Malaria are reported each year, with more than 80% of these found in Sub-Saharan Africa. The anopheline mosquito transmits malaria, and breeds in areas of shallow surface water that are suitable to the mosquito and parasite development. These environmental factors can be detected with satellite imagery, which provide enhanced spatial and temporal coverage of most of the earth's surface. The combined use of remote sensing and GIS provides an effective tool for monitoring environmental conditions that are conducive to malaria, and mapping the disease risk to human populations. <p> Since many vector-borne diseases such as malaria are prevalent in tropical areas, persistent cloud cover often presents a challenge to remote sensing operations. Radar remote sensing has the capability of penetrating clouds, providing a solution to the cloud-cover problem often experienced with optical satellite remote sensing. This research investigates the use of RADARSAT-1 data for monitoring and mapping malaria risk in coastal Kenya. An object-oriented approach to image classification is taken in order to circumvent some of the limitations of traditional pixel-based classification of radar imagery. GIS routines are used to assess how classified land cover variables relate to the presence and abundance of malaria-carrying mosquitoes and their proximity to populated areas, in order to generate a malaria risk map.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".