Opportunities and Challenges for Dietary Arsenic Intervention
Bibliographic record
Abstract
The diet is emerging as the dominant source of arsenic exposure for most of the U.S. population. Despite this, limited regulatory efforts have been aimed at mitigating exposure, and the role of diet in arsenic exposure and disease processes remains understudied. In this brief, we discuss the evidence linking dietary arsenic intake to human disease and discuss challenges associated with exposure characterization and efforts to quantify risks. In light of these challenges, and in recognition of the potential longer-term process of establishing regulation, we introduce a framework for shorter-term interventions that employs a field-to-plate food supply chain model to identify monitoring, intervention, and communication opportunities as part of a multisector, multiagency, science-informed, public health systems approach to mitigation of dietary arsenic exposure. Such an approach is dependent on coordination across commodity producers, the food industry, nongovernmental organizations, health professionals, researchers, and the regulatory community. https://doi.org/10.1289/EHP3997.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".