Isotopic Composition of Hg in Fogwaters of Coastal California
Bibliographic record
Abstract
We present the first measurements of the isotopic composition of mercury (Hg) in fogwater, measured in samples collected across coastal California. Coastal California fogwater samples exhibit a relatively narrow range of Hg isotopic compositions {δ 202 Hg = −0.60‰ to 0.38‰, average of −0.10 ± 0.33‰ [one standard deviation (1SD)]; Δ 199 Hg = 0.04–0.75‰, average of 0.28 ± 0.17‰ (1SD); Δ 200 Hg = −0.16‰ to 0.24‰, average of 0.08 ± 0.10‰ (1SD)}. The isotopic composition of fogwater samples did not exhibit any spatial trends, either with distance from the Pacific Ocean coastline or with the latitude of sampling locations. The Hg isotopic composition of coastal California fogwater samples is not significantly different from that of precipitation collected across coastal California and the North Pacific, suggesting that marine-derived atmospheric Hg has a relatively homogeneous isotopic composition across the North Pacific. Fogwater samples exhibit a Δ 199 Hg/Δ 201 Hg slope of 1.03 consistent with Hg(II) photoreduction, highlighting the importance of photoreduction mechanisms in controlling the odd-MIF isotopic composition of atmospheric Hg wet deposition. Overall, the need for an improved understanding of the processes that control atmospheric Hg deposition is revealed by this data set, as such knowledge will be required to more accurately model global atmospheric Hg cycling.
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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 | 0.001 | 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".