Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Regional and local sources contributing to gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) at an urban coastal site in Dartmouth, Nova Scotia, Canada were investigated using the Concentration-Weighted Trajectory model (CWT) and Conditional Probability Function. From 2010–2011, GEM, GOM, and PBM concentrations were 1.67 ± 1.01 ng m−3, 2.07 ± 3.35 pg m−3, and 2.32 ± 3.09 pg m−3, respectively. Seasonal variability was observed, with statistically higher GEM and PBM concentrations in winter and spring and higher GOM in spring. In the CWT, concentrations are the weighting factors for the trajectory residence time in modeled grid cells, which results in the identification of source areas based on the CWT values in the grid cells. Potential source areas were identified in regions with known industrial Hg sources particularly in the fall season, but also in regions without these sources (e.g. Atlantic Ocean, northern Ontario and Quebec). CWTs for GOM and PBM that were associated with ≥ 5 kg industrial Hg emissions from 2010–2011 were statistically larger than those with zero Hg emissions, despite a lack of strong correlations. A large proportion of elevated CWTs (85–97%) was in regions with zero industrial Hg sources indicating the potential role of non-point sources, natural emissions, and residential-scale combustion. Analysis of wind data suggests that a commercial harbor and vehicular traffic were potential local sources. Evaluating modeled source areas against Hg emissions inventories was not an ideal method for assessing the CWT model accuracy because of insufficient data on Hg emissions at more precise locations.
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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 | 0.000 |
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 it