Bibliographic record
Abstract
Arsenic speciation in a variety of commercial algal products and a brown alga, Fucus gardneri, collected in Vancouver, B.C., was carried out by using high performance liquid chromatography-inductively coupled plasma-mass spectrometry (HPLC-ICP-MS)and hydride generation atomic absorption spectrometry (HGAAS). Water-soluble organoarsenic compounds present in commercially available food products made from red algae, brown algae and blue-green algae were analyzed by using HPLC-ICP-MS. By the application of two HPLC columns and two mobile phase conditions, arsenosugars (arsenoribofuranosides) in a variety of algae were identified by comparing the retention times with the organoarsenic compounds previously identified in an oyster tissue standard reference material, NIST 1566a. A commercial brown algal product, kelp powder, was found to contain four different arsenosugars. This product may have potential as a "standard reference material" for identification purposes. A terrestrial blue-green alga, Nostoc commune var flagelliforme, was also analyzed and found to contain an arsenosugar, a compound which was previously known only in marine organisms. The total arsenic content as well as the amounts of water-soluble arsenic compounds in all commercial products were determined by using a continuous flow HGAAS system. Commercial marine algae were found to contain high amounts of total arsenic, from 7.6 μg g⁻¹ to 49.3 μg g⁻¹. The terrestrial product was found to contain only 2.7 μg g⁻¹ of total arsenic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".