Predicting Net Mercury Methylation in Sediments Using Diffusive Gradient in Thin Films Measurements
Bibliographic record
Abstract
Diffusive gradient in thin film (DGT) sediment probes for methylmercury (MMHg) were successfully deployed for up to 30 h in three mudflat sediments in San Francisco Bay for measuring labile fractions of dissolved MMHg in pore water. Our calculations show that the local DGT-induced depletion of MMHg in sediment pore waters should be fully compensated by the natural in situ MMHg production and its subsequent remobilization from the solid phase. DGT results were interpreted in terms of labile pore water concentration and provide MMHg concentration depth profiles with a centimeter resolution. Average concentrations of DGT-labile MMHg were 2.10 ± 0.29 and 1.64 ± 0.30 ng L(-1) at China Camp and Hamilton Army Airfield sediment pore waters, respectively, while the riverine location at Petaluma showed a distinct peak of 7.1 ng L(-1) near the sediment surface. Using isotope-enriched mercury species, high resolution depth profiles of MMHg net production rates ranging from 0.2 to 9.8 ng g(-1) d(-1) were obtained in parallel sediment cores sampled closely to DGT deployment sites. A positive, linear relationship between MMHg net production rates and labile MMHg concentrations acquired through DGT measurements was found and explained 79% of the variability in the data set. The latter illustrates that mercury methylation, a biogeochemical process, strongly affected the quantity of MMHg accumulated by the DGT device in the sediment and suggests that DGT measurements have the potential to predict net methylation rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".