Evasion of added isotopic mercury from a northern temperate lake
Bibliographic record
Abstract
Isotopically enriched Hg (90% 202Hg) was added to a small lake in Ontario, Canada, at a rate equivalent to approximately threefold the annual direct atmospheric deposition rate that is typical of the northeastern United States. The Hg spike was thoroughly mixed into the epilimnion in nine separate events at two-week intervals throughout the summer growing season for three consecutive years. We measured concentrations of spike and ambient dissolved gaseous Hg (DGM) concentrations in surface water and the rate of volatilization of Hg from the lake on four separate, week-long sampling periods using floating dynamic flux chambers. The relationship between empirically measured rates of spike-Hg evasion were evaluated as functions of DGM concentration, wind velocity, and solar illumination. No individual environmental variable proved to be a strong predictor of the evasion flux. The DGM-normalized flux (expressed as the mass transfer coefficient, k) varied with wind velocity in a manner consistent with existing models of evasion of volatile solutes from natural waters but was higher than model estimates at low wind velocity. The empirical data were used to construct a description of evasion flux as a function of total dissolved Hg, wind, and solar illumination. That model was then applied to data for three summers for the experiment to generate estimates of Hg re-emission from the lake surface to the atmosphere. Based on ratios of spike Hg to ambient Hg in DGM and dissolved total Hg pools, ratios of DGM to total Hg in spike and ambient Hg pools, and flux estimates of spike and ambient Hg, we concluded that the added Hg spike was chemically indistinguishable from the ambient Hg in its behavior. Approximately 45% of Hg added to the lake over the summer was lost via volatilization.
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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".