Heterogeneity of malaria prevalence in alluvial gold mining areas in Northern Mato Grosso State, Brazil
Bibliographic record
Abstract
This paper analyzes factors affecting the risk of malaria among individuals working in wildcat gold mining camps (garimpos) in northern Mato Grosso State in the Brazilian Amazon. Historically, such mining camps have the locations with the highest malaria prevalence in the Brazilian Amazon. However, little attention has focused on understanding the disease from the internal perspective of the mining camps themselves, such as the mining population's characteristics and its spatial organization. This paper adopts a stepwise logistic model to identify spatial, occupational-exposure, and cultural factors that affect malaria prevalence. According to the results, differences among individuals working and/or living in the gold mining areas could produce different exposure to the disease and thus to different risk of malaria prevalence. Understanding these differences may provide an important tool for identifying risk profiles in the gold mining and related population and for informing programs for prevention and treatment of malaria in the Amazon.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".