Photodecomposition of Methylmercury in Atmospheric Waters
Bibliographic record
Abstract
Experiments were conducted to empirically examine net changes in methylmercury concentration of atmospheric waters as function of irradiance. Methods were developed to allow experiments to be conducted at atmospherically relevant concentrations using trace metal clean techniques, over a range of aqueous matrices. Rain water was collected at Devil’s Lake State Park, WI, and simulated cloud water was created by water extraction of particulate matter collected at the same site. These waters were spiked with methyl mercury chloride and mercuric chloride and exposed to sunlight on the roof of a building. Experiments were conducted during typical summer conditions with respect to temperature, sunlight intensity and sunlight duration. For all cases, exposure to sunlight resulted in net loss of methylmercury: –0.022 ± 0.002 1/hr in rainwater at a total UVB flux of 8 kWhrs/m2; –0.008 ± 0.001 1/hr in simulated cloud water at a total UVB flux of 5.5 kWhrs/m2. For dark cases, no statistically significant formation in methylmercury from inorganic mercury was detected. Furthermore, laboratory experiments to form methylmercury from mercuric-acetate complexes did not give detectable yields. Given the results of this study, and the results of studies cited in this article, it is unlikely that homogeneous MeHg formation is fast enough to lead to the net formation of MeHg in atmospheric waters exposed to sunlight.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Experimental atmospheric chemistry on photodecomposition of methylmercury; the object is a chemical process.
This is an environmental chemistry experiment about methylmercury in atmospheric waters, not research practice.
Environmental chemistry experiment on methylmercury photodecomposition; domain science, not research as object.
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 | 0.000 | 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".