Determination of Mercury in Ayurvedic Dietary Supplements That Are Not Rasa Shastra Using the Hydra-C Direct Mercury Analyzer
Bibliographic record
Abstract
Mercury has been determined in Ayurvedic dietary supplements (Trifala, Trifala Guggulu, Turmeric, Mahasudarshan, Yograj, Shatawari, Hingwastika, Shatavari, and Shilajit) by inductively coupled plasma-mass spectrometry (ICP-MS) and direct mercury analysis using the Hydra-C direct mercury analyzer (Teledyne Leeman Labs Hudson, NH, USA). Similar results were obtained from the two methods, but the direct mercury analysis method was much faster and safer and required no microwave digestion (unlike ICP-MS). Levels of mercury ranged from 0.002 to 56 μ g/g in samples of dietary supplements. Standard reference materials Ephedra 3240 and tomato leaves that were from the National Institute of Standard and Technology (NIST) and dogfish liver (DOLT3) that was from the Canadian Research Council were analyzed using Hydra-C method. Average mercury recoveries were 102% (RSD% 0.0018), 100% (RSD% 0.0009), and 101% (RSD% 0.0729), respectively. Hydra-C method Limit Of Quantitation was 0.5 ng.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".