Poverty dynamics, arsenic exposure and adolescent mortality: A prospective finding
Bibliographic record
Abstract
Earlier we reported in 2019 that higher mortalities in young adults who were exposed to higher arsenic through drinking water. Widespread arsenic contamination in underground water is a well-documented public health concern that threatens millions of lives worldwide. In other word, Bangladesh belongs to low-middle income country (LIMC) and poverty is still high in Bangladesh. One in five individuals are living below the national poverty line. In other word, Poverty, often linked to chronic diseases, is a multi-dimensional phenomenon. Although it is evident that high levels (> 300 µg/L) of arsenic exposure from drinking water are related to adverse health outcomes, health effects of arsenic exposure at low-to-moderate levels (10–300 µg/L) are not well understood. Since poorer people living in the rural areas have limited access to safe drinking water and had to rely on the tubewell water which is the point of arsenic commination in humans. Therefore, research is warranted to understand the additional sufferings of the poor segment of the society due to chronic high level arsenic exposure. Given the complex scenario, we investigated if poverty dynamic plays a role in young adult deaths in Bangladesh.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".