Arsenic Speciation of Contaminated Soils/Solid Wastes and Relative Oral Bioavailability in Swine and Mice
Bibliographic record
Abstract
Arsenic (As) is one of the most widespread, toxic elements in the environment, and human activities have resulted in a large number of contaminated areas. However abundant, the potential of As toxicity from exposure to contaminated soils is limited to the fraction that will dissolve in the gastrointestinal system and be absorbed into systemic circulation or bioavailable species. In part, the release of As from contaminated soil to gastrointestinal fluid depends on the form of solid phase As, also termed “As speciation”. In this study, 27 As-contaminated soils and solid wastes were analyzed using X-ray absorption spectroscopy (XAS) and results were compared to in vivo bioavailability values determined using the adult mouse and juvenile swine bioassays. Arsenic bioavailability was lowest for soils that contained large amounts of arsenopyrite and highest for materials that contained large amounts of ferric arsenates. Soil and solid waste type and properties rather than the contamination source had the greatest influence on As speciation. Principal component analysis determined that As(V) adsorbed and ferric arsenates were the dominant species that control As speciation in the selected materials. Multiple linear regression (MLR) was used to determine the ability of As speciation to predict bioavailability. Arsenic speciation was predictive of 27% and 16% of Relative Bioavailable (RBA) As determined using the juvenile swine and adult mouse models, respectively. Arsenic speciation can provide a conservative estimate of RBA As using MLR for the juvenile swine and adult mouse bioassays at 55% and 53%, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".