Bibliographic record
Abstract
Mercury (Hg) is a pollutant of global concern due to its high toxicity at low concentrations. Most emissions of Hg are to the air, however, the majority of human exposure to Hg is through the consumption of contaminated fish. Therefore, to link human exposure of Hg to emissions requires understanding its environmental cycle. The focus of this research is on fluxes of Hg in the urban environment with a particular focus on Hg air-to-surface deposition, surface-to-air emission, and surface-to-water washoff. The main study location was Toronto, Ontario, however samples were also collected from Austin, Texas. Mercury was found to deposit and accumulate on urban surfaces at a linear rate over time when not exposed to influence of washoff. The accumulation of Hg on urban surfaces was spatially variable throughout the urban environment (0.82 ng/m2/d to 2.8 ng/m2/d) and significantly higher compared a rural location (0.58 ng/m2/d). The surface-to-air emission of Hg from pavement was spatially variable throughout the city (median range from below the detection limit to 5.2 ng/m2/h), and was related to the concentration of Hg associated with street dust. The amount of solar radiation was also shown to have a significant influence on the emission of Hg from the surface. During rain events, high Hg concentrations were measured at the beginning of runoff (up to 76.0 ng/L), which quickly decreased during the remainder of the event. The flux of Hg was driving by the discharge and the largest Hg loads were always associated with the period of peak discharge. Depending on the rain event characteristics, the urban surface was a net sink or a net source of Hg. Simulated rain experiments on an urban roadway showed that the number of antecedent dry days was associated with the amount of Hg mobilized in runoff. The simulated rain events also determined that the Hg in rain became quickly bound to surface particles and that the majority of Hg in runoff was derived from the surface.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".