Determining Total Mercury in Samples from the Persian Gulf and the Caspian Sea: Comparison of Dry Ash and Wet Extraction Mothods
Bibliographic record
Abstract
Monitoring of mercury in environmental samples, with its proven toxicity on the food chain, requires sensitive and accurate analytical techniques. In this study, two methods for identification and quantification of total mercury in biological and sediment samples are compared: i) dry ash preparation and subsequent established procedures for Combustion Atomic Absorption Spectrometry, (AMA 254) and ii) wet extraction consisted of classical and microwave oven digestion methods combined with cold vapor Atomic Absorption (CV AAS) analyzing method. In order to compare the accuracy of the methods for total mercury determination, biological and sediment certified Reference Materials and environmental samples were analyzed. The precision and accuracy of the applied analytical methods were compared on eight Certified Reference Materials, Dorm2, Dolt 2 and Tort 2, trace metals in dog fish muscle, liver and Lobster (provided by the national research council of Canada); NIST 1556a, NISTt-2976, IAEA 142 trace metals and mercury in oyster, mussels and fish, respectively; IAEA 086 and IAEA405 trace elements in hair and in polluted estuary sediment (provided by international Atomic Energy), respectively. Ten environmental (fish and sediment) samples and one inter-comparison sample (BCR 710, Total and methylmercury in oyster tissue) as well as certified samples were analyzed in six replications. The detection limits (DLs) in the combustion AAS (AMA), CEM (MDS 2000) microwave oven CVAAS, CEM (Mars 5) microwave oven CV AAS and in normal oven CV AAS for biological and sediment were compared. The AMA 254 with lower detection and quantification limits was more sensitive analyzing method in comparing with the other methods.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".