Total gaseous mercury (TGM) concentration over Lake Superior and Lake Michigan
Bibliographic record
Abstract
Mercury-contaminated fish are a serious problem in the Great Lakes basin, because mercury is a potent neurotoxin that poses a danger to both humans and wildlife. Lake Superior lake trout and walleye have the highest mercury concentrations of the five Great Lakes. Because the atmosphere is the major source of mercury to the Great Lakes, information on the over-water mercury concentration is essential to model the mercury biogeochemical cycle. For the first time in the peer-reviewed literature, this paper presents total gaseous mercury (TGM) measurements made over Lake Superior and Lake Michigan. The Lake Superior aircraft measurements were made at an altitude of 300 m, and the Lake Michigan aircraft measurements at a variable altitude of 30–300 m. The over-water Lake Superior TGM of 1.02 ± 0.34 ng/m3 is much lower than the TGM from nine stations in the Canadian Atmospheric Mercury Measurement Network (CAMNet) and six stations in the Atmospheric Mercury Network (AMNet). The land-based TGM concentrations average range from 1.25 to 1.75 ng/m3 which are in good agreement with current global average values of 1.3–1.6 ng/m3. The over-water Lake Michigan TGM is 1.65 ± 0.61 ng/m3. We also present Lake Superior over-water measurements of volatile organic compounds (VOC), ozone (O3), nitrogen oxide (NOy), and particulate matter. Elemental carbon (EC) is a tracer for mercury because mercury is released during the combustion of coal. EC is significantly correlated with TGM over both Lake Superior and Lake Michigan. TGM over Lake Michigan is also significantly correlated with organic carbon, sulfate, nitrate and ammonium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".