Determination of an arsenosugar in oyster extracts by liquid chromatography-electrospray mass spectrometry and liquid chromatography-ultraviolet photo-oxidation-hydride generation atomic fluorescence spectrometry
Bibliographic record
Abstract
HPLC-UV-HG-AFS analysis of aqueous extracts of oysters (Crassostrea gigas) taken from the southwestern Atlantic coast of Spain showed the presence of arsenite, arsenate, dimethylarsinic acid and an unidentified arsenic peak. Subsequent analysis of the oyster samples by LC-electrospray MS and comparison with four standard dimethylarsinoylribosides (arsenosugars), showed that the previously unidentified peak was an arsenosugar (arsenosugar 2). When the arsenosugar in the oyster was quantified using the two detection methods and external calibration with standard arsenosugar, there was a large discrepancy between the two sets of results. The LC-MS analysis was strongly affected by the sample matrix and gave concentrations 50% lower than those obtained by AFS detection. When the method of standard addition was applied to the LC-MS analysis, the results were comparable to the AFS data. The matrix effects were eliminated by subjecting the extract to a clean-up procedure with anion-exchange and gel permeation preparative chromatography before the LC-MS analysis. The arsenosugars gave a small signal without photo-oxidation when they were analysed by HPLC-HG-AFS. Possibly this resulted from partial decomposition of the arsenosugar to dimethylarsinic acid under the acidic conditions employed in the hydride generation step.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".