Quantifying the effects of photoreactive dissolved organic matter on methylmercury photodemethylation rates in freshwaters
Bibliographic record
Abstract
Abstract The present study examined potential effects of seasonal variations in photoreactive dissolved organic matter (DOM) on methylmercury (MeHg) photodemethylation rates in freshwaters. A series of controlled experiments was carried out using natural and photochemically preconditioned DOM in water collected from 1 lake in June, August, and October. Natural DOM concentrations doubled between June and August (10.2–21.2 mg C L−1) and then remained stable into October (19.4 mg C L−1). Correspondingly, MeHg concentrations peaked in August (0.42 ng L−1), along with absorbances at 350 nm and 254 nm. Up to 70% of MeHg was photodemethylated in the short 48-h irradiation experiments, with June having significantly higher rates than the other sampling months (p < 0.001). Photodemethylation rate constants were not affected by photoreactive DOM, nor were they affected by initial MeHg concentrations (p > 0.10). However, MeHg photodemethylation efficiencies (quantified in moles MeHg lost/moles photon absorbed) were higher in treatments with less photoreactive DOM. Congruently, MeHg photodemethylation efficiencies also decreased over summer by up to 10 times across treatments in association with increased photoreactive DOM, and were negatively correlated with DOM concentration. These results suggest that an important driver of MeHg photodemethylation is the interplay between MeHg and DOM, with greater potential for photodemethylation in freshwaters with more photobleached DOM and lower DOM content. Environ Toxicol Chem 2017;36:1493–1502. © 2016 SETAC
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".