Comparisons of mercury sources and atmospheric mercury processes between a coastal and inland site
Bibliographic record
Abstract
Comparisons of mercury sources and atmospheric mercury processes were conducted between a coastal and inland site in northeastern North America. Identifying sources of atmospheric Hg is essential for understanding what is potentially contributing to Hg bioaccumulation at these two sites. A data set consisting of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), particle‐bound mercury, ozone, trace gases, particulate ions, and meteorological data were analyzed using principal components analysis (PCA), absolute principal component scores (APCS), and back trajectories. The PCA factors representing gaseous Hg condensation on particles during winter and combustion and industrial sources were found at both sites. However, the PCA factor for combustion/industrial sources was not found in 2010 at either site, likely because of SO2 emissions reductions from coal utilities from 2008 to 2010. Using APCS and back trajectories, the combustion/industrial factor at the coastal site was narrowed down to shipping ports along the Atlantic coast. Hg sources affecting coastal sites are different from those affecting inland sites because of the influence of marine airflows. GEM evasion from the ocean was evident from a PCA factor containing GEM, relative humidity, wind speed, and precipitation along with significantly higher contributions of this source (APCS) from oceanic trajectories compared to land/coastal trajectories. Analysis of the effects of ozone and water vapor mixing ratio on %GOM/total gaseous mercury suggest that Hg‐Br photochemistry occurred at lower ozone concentrations (<40 ppb) at the coastal site and the absence of free troposphere transport of GOM.
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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 | 0.000 | 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".