Continuous Flow–Double Purge and Trap Method for Preconcentrating Mercury in Large Volumes of Seawater for Stable Isotope Analysis
Bibliographic record
Abstract
Mercury (Hg) isotopes provide a useful tool to understand Hg sources and processes in the environment. The Hg isotopic composition of seawater remains poorly constrained due to the lack of an efficient method to process large volumes of low-Hg-concentration seawater samples. Here, we develop a continuous flow – double purge and trap device for the in situ preconcentration of Hg in seawater. This method yielded a good Hg recovery of 91.7 ± 3.3% ( n = 4, 1SD) for spiked seawater samples and gave reasonably similar Hg isotope ratios of NIST 8610, indicating a limited matrix effect and limited Hg isotope fractionation during processing of seawater. NIST 8610 δ 202 Hg (−0.55 ± 0.09‰, n = 4, 1SD) and Δ 199 Hg (0.07 ± 0.02‰, n = 4, 1SD) were similar to previously published data. The method was successfully applied to seawater collected from the Xiamen Bay and the South China Sea. The seawater samples showed a Hg recovery of 91.6 ± 5.4% ( n = 12, 1SD). Seawater Δ 199 Hg (−0.04 ± 0.05‰, n = 7, 1SD) in the Xiamen Bay was different from seawater Δ 199 Hg (0.05 ± 0.07‰, n = 5, 1SD) in the South China Sea, which implies distinct Hg sources to coastal and open ocean areas and highlights the robustness of our method in understanding the Hg isotopic composition of seawater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".