Comparison between Back-Trajectory Based Modeling and Lagrangian Backward Dispersion Modeling for Locating Sources of Reactive Gaseous Mercury
Bibliographic record
Abstract
Reactive gaseous mercury (RGM) was measured using an annular denuder coated with potassium chloride at three rural sites (Potsdam, Stockton, and Sterling) in New York State from April 2002 to April 2003. Concentrations of RGM ranged from 0.1 to 84.6 pg m(-3) with large spatial and temporal variation. Potential source contribution function (PSCF), a common receptor modeling tool, was used with these measurements, and source-receptor relationships were calculated using back-dispersion and deposition as well as back-trajectories. Modeling results were compared with the RGM emissions inventory, and Spearman rank-order correlation coefficients were calculated. PSCF results incorporating backward dispersion and deposition were better correlated with the emissions inventory than PSCF based on back-trajectories alone. This difference was determined to be mainly due to the inclusion of dispersion rather than deposition. The main sources of RGM were suggested to be coal-fired power plants in New York and Pennsylvania, the large copper smelter in Quebec, and the taconite mining areas around the Great Lakes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".