Dissolved Gaseous Mercury Concentrations and Mercury Volatilization in a Frozen Freshwater Fluvial Lake
Bibliographic record
Abstract
In situ mesocosm experiments were performed to examine dissolved gaseous mercury (DGM), mercury volatilization, and sediment interactions in a frozen freshwater fluvial lake (Lake St. Louis, Beauharnois, QC). Two large in situ mesocosm cylinders, one open-bottomed and one close-bottomed (no sediment diffusion), were used to isolate the water column and minimize advection. Mercury volatilization over the closed-bottom mesocosm did not display a diurnal pattern and was low (mean = -0.02 ng m(-2) h(-1), SD = 0.28, n=71). Mercury volatilization over the open-bottom mesocosm was also low (mean = 0.24 ng m(-2) h(-1), SD = 0.08, n=96) however a diurnal pattern was observed. Low and constant concentrations of DGM were observed in surface water in both the open-bottomed and close-bottomed mesocosms (combined mean = 27.6 pg L(-1), SD = 7.2, n=26). Mercury volatilization was significantly correlated with solar radiation in both the close-bottomed (Pearson correlation = 0.33, significance = 0.005) and open-bottomed (Pearson correlation = 0.52, significance = 0.001) mesocosms. However, DGM and mercury volatilization were not significantly correlated (at the 95% level) in either of the mesocosms (significance = 0.09 in the closed mesocosm and significance = 0.9 in the open mesocosm). DGM concentrations decreased with depth (from 62 to 30 pg L(-1)) in the close-bottomed mesocosm but increased with depth (from 30 to 70 pg L(-1)) in the open-bottomed mesocosm suggesting a sediment source. DGM concentrations were found to be high in samples of ice melt (mean 73.6 pg L(-1), SD = 18.9, n=6) and snowmelt (mean 368.2 pg L(-1), SD = 115.8, n=4). These results suggest that sediment diffusion of mercury and melting snow and ice are important to DGM dynamics in frozen Lake St. Louis. These processes may also explain the lack of significant correlations observed in the DGM and mercury volatilization data.
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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 | 0.000 |
| 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) | 0.000 | 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".