Mercury uptake within an ice algal community during the spring bloom in first‐year Arctic sea ice
Bibliographic record
Abstract
In this study, we examine mercury bioaccumulation by a first‐year sea‐ice (FYI) algal community in the western Canadian Arctic during the spring of 2008. Total mercury concentration in bottom sea‐ice particulate ([PHg]T) ranged 0.004–0.022 µg/g dw and was limited by the amount of mercury available for uptake when the spring bloom commenced. Mercury in ice algae originated from a combination of brine and seawater as sources, while atmospheric mercury depletion events did not appear to significantly contribute as a source in a coupled manner. We show that the bottom sea‐ice brine presents a chemically and biologically unique niche from which inorganic Hg makes its way into the food web. Once incorporated into algae, mercury can be transported spatially and trophically within the ecosystem by a range of processes including grazing, resuspension, remineralization, and sedimentation. Ice algae contribute 10–60% of the annual primary production in the Arctic and are thought to become even more productive and abundant under a mild climate change scenario. Replacement of multiyear ice with FYI in the Beaufort Sea alone could result in an additional influx of ∼48 kg/yr of particle bound Hg. Further studies are thus warranted to elucidate mechanisms by which mercury transformation processes and transfer into the Arctic marine food web are impacted by the interaction between sea ice, brine, and seawater.
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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 | 0.002 | 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) | 0.000 | 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".