Bioavailable Mercury in Arctic Snow Determined by a Light-emitting <i>mer-lux</i> Bioreporter
Bibliographic record
Abstract
... The initial objective of this component of my research was to determine the bioavailability of Hg(II) in snow entering the Arctic via long-range atmospheric transport. In addition to samples for bioHg, snow samples were collected for total Hg, Me Hg, and major cation chemistry. Polar sunrise at Barrow is in late January, and the melt period begins in June. Samples were therefore collected before Polar sunrise in January and after Polar sunrise in March, May, and June 2000. BioHg was undetectable in Barrow snow in January, and total Hg concentrations were low. BioHg then increased from 0.22 ng/L (~1% of total Hg) in March to 8.8 ng/L (nearly 13% of the total Hg) in May (Fig. 2). (Rarely have the environmental samples that I have analyzed exceeded 0.5 ng/L.) Our June snow sample was taken just before the intensive snowmelt period began, so the snow was slushy but not melted. BioHg had decreased to 2.9 ng/L, which is still very high for a remote area. Furthermore, this concentration represented over 50% of the total Hg in Barrow snow. Because Barrow has sunlight 24 hours a day during the melt period, melting occurs over a relatively short time. If these concentrations of bioHg are sustained during this period, a very large pulse must be entering the ecosystem in the spring. (We will be examining the melt period more intensively in 2001; see below.) An interesting and unexpected finding was that during Polar sunrise, MeHg also increased to concentrations commonly found in boreal wetlands where it is biotically produced. The mechanism of MeHg formation in the Arctic atmosphere is as yet unknown; however, we hypothesize that it could involve the demethylation of dimethyl mercury (diMeHg) produced biogenically in the ocean....
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".