Photochemical aqueous mercury removal: effects of DOM and DO
Bibliographic record
Abstract
Photochemical reactions between mercury (Hg) and dissolved organic matter were studied to understand what conditions would promote mercury volatilisation from solution. Prepared solutions of mercury (II) nitrate and humic acid (HA) (at different ratios) were exposed to 254-nm ultraviolet irradiation and a continuous purge with nitrogen, air or oxygen gas to create three different dissolved oxygen (DO) concentrations. As HA was introduced into the system with a nitrogen purge, the overall quantity of dissolved gaseous mercury after 60 min, and subsequent mercury removal, decreased. An analysis of variance indicated that there is only a 61% confidence level that the 1:10 and the 1:100 mercury–HA ratios are statistically different, suggesting that the decrease in mercury removal as HA concentration increased cannot be solely attributed to mercury binding with sulfur or other functional groups on the HA. Experiments indicated a positive correlation of HA oxidation as DO increased with an air and oxygen purge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".