X-ray Absorption Near-Edge Structure Analysis of Arsenic Species for Application to Biological Environmental Samples
Bibliographic record
Abstract
Arsenic is an element that is ubiquitous in the environment and is known to form compounds with toxic, even carcinogenic properties. Arsenic toxicity is a function of its chemical form (species). Identification of arsenic species is necessary to accurately determine the transformation and fate of arsenicals as well as the actual risk posed by arsenic contamination. We report X-ray absorption near-edge structure (XANES) measurements of 16 biologically important arsenic compounds. Solid and aqueous standards were studied for differences in XANES spectral features, white line positions, stability during exposure to the beam, and stability between two beam exposures separated by 48 h. Samples containing As(III) (11870.0-11871.7+/-0.5 eV) and As(V) (11872.6-11875.3+/-0.5 eV) were easily distinguished by white line energies and could be further subdivided into a total of seven groups. Valuable examples include As(III)-sulfur compounds (11870.0+/-0.5 eV), arsenobetaine and arsenocholine (11872.6+/-0.5 eV), and a dimethyl arsinyl riboside (11873.3+/-0.5 eV). A growing number of environmental and biological studies use X-ray absorption spectroscopy (XAS) results to complement their more traditional analyses. Results provided here are intended to help make XAS more accessible to new users interested in the study of arsenic in the environment.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".