Characterizing arsenic in preserved hair for assessing exposure potential and discriminating poisoning
Bibliographic record
Abstract
Advanced analytical techniques have been used to characterize arsenic in taxidermy specimens. Arsenic was examined to aid in discriminating its use as a preservative from that incorporated by ingestion and hence indicate poisoning (in the case of historical figures). The results are relevant to museum curators, occupational and environmental exposure concerns, toxicological and anthropological investigations. Hair samples were obtained from six taxidermy specimens preserved with arsenic in the late 1800s and early 1900s to investigate the arsenic incorporation. The presence of arsenic poses a potential hazard in museum and private collections. For one sample, arsenic was confirmed to be present on the hair with time-of-flight secondary ion mass spectrometry and then measured with neutron activation analysis to comprise 176 microg g(-1). The hair cross section was analysed with synchrotron micro-X-ray fluorescence to investigate the transverse distribution of topically applied arsenic. It was found that the arsenic had significantly penetrated all hair samples. Association with melanin clusters and the medulla was observed. Lead and mercury were also identified in one sample. X-ray absorption near-edge spectroscopy of the As K-edge indicated that an arsenate species predominantly existed in all samples; however, analysis was hindered by very rapid photoreduction of the arsenic. It would be difficult to discriminate arsenic consumption from topically applied arsenic based on the physical transverse distribution. Longitudinal distributions and chemical speciation may still allow differentiation.
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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 | 0.000 |
| 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) | 0.001 | 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".