Introduction to public health and earth observation.
Bibliographic record
Abstract
Abstract This book chapter discusses research that has led to improved detection and control of infectious diseases and has expanded our knowledge of how these diseases emerge and re-emerge as driven by a combination of factors that include genetic change in causal pathogens, climate and other environmental changes, and changing human behavior. Earth Observation (EO) provides data at multiple spatial scales and is becoming a vital tool in helping us understand, track, and predict these diseases, allowing public health to proactively plan and implement informed interventions. Emerging infectious diseases pose continuous challenges to public health preparedness and policies and to programs for surveillance, prevention, and control. Infectious and chronic diseases are issues of concern for public health on a global, regional, and local level. More specifically, the book aims to: (i) assess current research and identify and document key themes; (ii) collate expert advice from the Canadian and international EO and public health communities on specific themes; and (iii) present conclusions and opportunities. The goal is to guide decision making on further research and on the development of innovative EO applications and solutions in the public health sector. To answer these questions, this book chapter identifies key public health activities in which EO data are or can be used. This includes prediction of disease emergence and spread and of disease forecasting to support public health programs for disease surveillance, prevention, and control interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.035 |
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".