Biological Chemistry of Toxic Metals and Metalloids, Such as Arsenic, Cadmium, and Mercury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Pollution is the largest environmental cause of disease and premature death in the world which causes an estimated nine million annual premature deaths. Owing to the exceptional longevity of highly toxic arsenic, cadmium, and mercury species in the environment, the Agency for Toxic Substances and Disease Registry (ASTDR) has ranked them within the top 7 chemicals of concern in terms of their frequency, toxicity, and potential for human exposure at national priority sites. Accordingly, the chronic exposure of human populations – including children – to these pollutants via the diet, drinking water and air is of increasing public health concern as it may be causally linked to human diseases, which have no known etiology. To unravel potential toxic metal exposure‐disease relationships, the elucidation of their bioinorganic chemistry in the bloodstream is critical as these processes determine which and how much of an absorbed toxic metal species and/or its metabolites will reach toxicological target organs. Considering the analytical complexity of this biological fluid, appropriate analytical techniques must be employed. We highlight advanced instrumental analytical approaches that can be used and provide examples of how their application has revealed new insight into the mechanisms of chronic toxicity of arsenic, cadmium and mercury species in mammals. A comprehensive understanding of the bioinorganic chemistry of individual toxic metal species is urgently needed to reassess current environmental regulations to reduce the global disease burden.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 it