Source-receptor relationships for speciated atmospheric mercury at the remote Experimental Lakes Area, northwestern Ontario, Canada
Bibliographic record
Abstract
Abstract. Source-receptor relationships for speciated atmospheric mercury measured at the Experimental Lakes Area (ELA), northwestern Ontario, Canada were investigated using various receptor-based approaches. The data used in this study include gaseous elemental mercury (GEM), mercury bound to fine airborne particles (<2.5 μm) (PHg), reactive gaseous mercury (RGM), major inorganic ions, sulphur dioxide, nitric acid gas, ozone, and meteorological variables, all of which were measured between May 2005 and December 2006. The source origins identified were related to transport of industrial and combustion emissions (associated with elevated GEM), photochemical production of RGM (associated with elevated RGM), road-salt particles with absorption of gaseous Hg (associated with elevated PHg and RGM), crustal/soil emissions, and background pollution. Back trajectory modelling illustrated that a remote site, like ELA, is affected by distant Hg point sources in Canada and the United States. The sources identified from correlation analysis, principal components analysis and K-means cluster analysis were generally consistent. The discrepancies between the K-means and Hierarchical cluster analysis were the clusters related to transport of industrial/combustion emissions, photochemical production of RGM, and crustal/soil emissions. Although it was possible to assign the clusters to these source origins, the trajectory plots for the Hierarchical clusters were similar to some of the trajectories belonging to several K-means clusters. This likely occurred because the variables indicative of transport of industrial/combustion emissions were elevated in at least two or more of the clusters, which means this Hg source was well-represented in the 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 | 0.001 |
| 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 | 0.001 | 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".