Human exposure to organic arsenic species from seafood
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
Seafood, including finfish, shellfish, and seaweed, is the largest contributor to arsenic (As) exposure in many human populations. In contrast to the predominance of inorganic As in water and many terrestrial foods, As in marine-derived foods is present primarily in the form of organic compounds. To date, human exposure and toxicological assessments have focused on inorganic As, while organic As has generally been considered to be non-toxic. However, the high concentrations of organic As in seafood, as well as the often complex As speciation, can lead to complications in assessing As exposure from diet. In this report, we evaluate the presence and distribution of organic As species in seafood, and combined with consumption data, address the current capabilities and needs for determining human exposure to these compounds. The analytical approaches and shortcomings for assessing these compounds are reviewed, with a focus on the best practices for characterization and quantitation. Metabolic pathways and toxicology of two important classes of organic arsenicals, arsenolipids and arsenosugars, are examined, as well as individual variability in absorption of these compounds. Although determining health outcomes or assessing a need for regulatory policies for organic As exposure is premature, the extensive consumption of seafood globally, along with the preliminary toxicological profiles of these compounds and their confounding effect on assessing exposure to inorganic As, suggests further investigations and process-level studies on organic As are needed to fill the current gaps in knowledge.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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