Vector-Borne Disease Risk Assessment in Africa: A Canadian Geomatics Approach
Bibliographic record
Abstract
Natural Resources Canada (NRCan) is strengthening partnerships with African countries and organizations by developing geomatics-based solutions for vector-borne disease risk assessment and mitigation. NRCan supports the use of geomatics technologies and is currently focused on fostering collaborative projects with African partners in areas where Canadian geomatics expertise can make a difference. As the world, and less-developed countries in particular, face serious health challenges from several devastating vector-borne diseases such as malaria, modern tools are needed to prevent and control epidemic situations and respond to serious disease outbreaks. Geomatics, in particular Earth observation and geographic information systems, are among today's advanced tools that can be used to gather and analyse disease-related environmental data, and provide useful information and solutions to practitioners and decision-makers. The Canadian Earth Sciences community has developed several geomatics-based tools and geospatial products that can be used to monitor health-related problems around the world. In particular, this paper will highlight the Canadian Earth observation satellite, RADARSAT-1, the Canadian Geospatial Data Infrastructure, GeoSemantica application, and some of the research that is currently on-going to make use of important geomatics technologies in the fight against vector-borne diseases in Africa
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".