Arsenic speciation in marine certified reference materials
Bibliographic record
Abstract
This study describes the analysis of arsenicals in three marine based certified reference materials (CRMs), DORM-2, DOLT-2 and TORT-2, all produced by the National Research Council of Canada. Extraction protocols involving accelerated solvent extraction (ASE) and sonication were investigated and arsenicals were determined on-line by anion-exchange and cation-exchange high-performance liquid chromatography (HPLC). The major species in all three tissues was found to be arsenobetaine (AsB), corresponding to 90%, 67% and 73% of the water-soluble arsenic in DORM-2, DOLT-2 and TORT-2, respectively. Other species quantified were arsenocholine (AsC+), tetramethylarsonium ion (TMAs+), trimethylarsonioproprionate (TMAP), dimethylarsenic acid (DMA), monomethylarsenic acid (MMA), arsenosugar D (As-sug D), arsenate (AsV) and arsenosugar A (As-sug A). The major species detected in all three materials were AsB and DMA, which together accounted for 97.4% (DORM-2), 95.4% (DOLT-2) and 91.7% (TORT-2) of the sum of the water soluble As species. The agreement for the determination of AsB by the different methods was good for all three materials, whereas some discrepancy was evident in the results for DMA. When an ASE approach with 50% acetic acid in methanol was used for the extraction, between 22% (DOLT-2) and 58% (TORT-2) more DMA was determined compared with extraction by sonication with water. Some discrepancy was evident between the sum of the species extracted and both the total As determined in the extracts and the certified As content based on complete digestion of the materials. The sum of the species recovered by sonication corresponded to 84% (DOLT-2) to 94% (DORM-2) of the total As determined in the aqueous extract. In comparison with the certified As content of the three materials, the sums of the extracted species range from 46% (DOLT-2) to 76% (TORT-2) and 102% (DORM-2).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".