Characterization of Arsenic Biotransformation Products from an Open Anaerobic Degradation of <i>Fucus distichus</i> by Hydride Generation Gas Chromatography Atomic Absorption Spectrometry and High Performance Liquid Chromatography Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
This work reports on the isolation and determination of biotransformation products obtained from the organoarsenic compounds that are present in Fucus distichus when it was subjected to an open anaerobic decomposition by using the Hydride Generation Gas Chromatography Atomic Absorption Spectrometry (HG-GC-AAS) and High Performance Liquid Chromatography Inductively Coupled Plasma Mass Spectrometry (HPLC-ICP-MS). The seaweed and filtrate residues obtained from the open anaerobic degradation procedure were extracted in methanol and partitioned in phenol-ether-water mixtures to obtain water soluble extracts. The water soluble extracts were cleaned up and separated on a gel permeation Sephadex G15 column. Arsenic species concentrations were determined by using HG-GC-AAS. Final characterization of the biotransformation isolates was carried out on HPLC-ICP-MS. Only two arsenic species, 2-dimethylarsinoyl ethanol (DMAE) and dimethylarsinic acid (DMAA), were positively identified in the water soluble extract of the marine brown algae. The two arsenic species are strong intermediate candidates in the biosynthesis of arsenobetaine from oceanic arsenate in marine food webs.
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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 | 0.000 | 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) | 0.000 | 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".