The Impact of Protected Areas on the Incidence of Infectious Diseases
Bibliographic record
Abstract
Abstract Background: The natural environment provides multiple ecosystem services, and thus welfare benefits. In particular, it is known that different ecosystems, such as forests, contribute to human health through different ecological interactions, and that degradation of these natural ecosystems have been linked to the emergence and re-emergence of infectious diseases. However, there is little evidence on how ecosystem conservation policies affect human health. In Chile, about 20% of national land is under protection by its national network of public protected areas. Methods: We use a database of mandatory reporting of diseases between 1999 and 2014, and considering socio-economic, demographic, climate and land-use factors to test for a causal relationship between protected areas and incidence of infectious diseases using negative binomial random effects models. Results: We find statistically significant effects of protected areas on a lower incidence of Paratyphoid and Typhoid Fever, Echinococcosis, Trichinosis and Anthrax. Conclusions: These results open the discussion about both causal mechanisms that link ecosystem protection with the ecology of these diseases and impacts of protected areas on further human health indicators. JEL Codes: Q58, Q57, Q56, Q01
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".