RADARSAT-1 for Monitoring Vector-borne Diseases in Tropical Environments: A Review
Bibliographic record
Abstract
The incidence and spread of vector-borne infectious disease is an increasing concern in many parts of the world, especially tropical areas. Earth observation techniques are becoming a recognised means of monitoring and mapping disease risk, and have proven useful in associating environmental indicators with various disease and their vectors. Geographically, the areas that bare the burden of infectious disease are often remote and not easily monitored using traditional, labour intensive survey techniques. High spatial and temporal coverage provided by spaceborne sensors allows for the investigation of large areas in a timely manner. Since the majority of infectious diseases occur in topical areas, however, one of the main barriers to earth observation techniques is high cloud-cover. Synthetic Aperture Radar (SAR) technology offers a solution to this problem by providing all-weather, day and night imaging capability. RADARSAT- 1, Canada's first Earth observation satellite is being used operationally for many applications, including flood monitoring, land cover mapping and disaster management. This paper will discuss several SAR remote-sensing applications and consider the potential of RADARSAT-1 for disease monitoring applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".