Bibliographic record
Abstract
Abstract Urban stormwater runoff has been identified as a leading cause of waterway impairment for many pollutants, but there has been a lack of research that directly measures Hg in urban stormwater runoff. The objectives of this research were: to use high frequency sampling to characterize the Hg export dynamics from an urban micro-catchment (i.e. a parking lot) during individual rain events; determine the relationship between suspended sediments and Hg transport; assemble event-scale mass balances of atmospheric Hg inputs, surface storage, and Hg export in runoff to evaluate the relative importance of rainfall-derived Hg and surface-derived Hg in runoff; and finally, to compare the yield of Hg from the urban micro-catchment to that of a larger mixed land-use urban catchment to evaluate the feasibility of scaling the results. The results found that the highest Hg concentrations in runoff were observed during the rising limb of the hydrograph (first flush effect), which was dominated by particulate bound Hg (84 ± 8%). There was a significant relationship between the Hg and total suspended solids (TSS) concentrations in runoff. For all events, the largest Hg flux occurred during the period of peak discharge, even though the Hg concentrations were substantially lower during this period. The catchment surface Hg load (i.e. street dust) varied over the course of the study, but the changes were not clearly linked to the rain events. The mass balance of the Hg inputs and outputs from the catchment showed that it could act as a Hg sink or a source depending on the rainfall characteristics. The export of Hg from the larger mixed land-cover catchment were all within the range of the values from the parking-lot catchment, though tended to be lower as a result of increased retention and fewer sources/disturbance within the catchment.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".