A mass balance inventory of mercury in the Arctic Ocean
Bibliographic record
Abstract
Environmental context. Mercury (Hg) occurs at high concentrations in Arctic marine wildlife, posing a possible health risk to northern peoples who use these animals for food. We find that although the dramatic Hg increases in Arctic Ocean animals since pre-industrial times can be explained by sustained small annual inputs, recent rapid increases probably cannot, because of the existing large oceanic Hg reservoir (the ‘flywheel’ effect). Climate change is a possible alternative force underpinning recent trends. Abstract. The present mercury (Hg) mass balance was developed to gain insights into the sources, sinks and processes regulating biological Hg trends in the Arctic Ocean. Annual total Hg inputs (mainly wet deposition, coastal erosion, seawater import, and ‘excess’ deposition due to atmospheric Hg depletion events) are nearly in balance with outputs (mainly shelf sedimentation and seawater export), with a net 0.3% year–1 increase in total mass. Marine biota represent a small fraction of the ocean’s existing total Hg and methyl-Hg (MeHg) inventories. The inertia associated with these large non-biological reservoirs means that ‘bottom-up’ processes (control of bioavailable Hg concentrations by mass inputs or Hg speciation) are probably incapable of explaining recent biotic Hg trends, contrary to prevailing opinion. Instead, varying rates of bioaccumulation and trophic transfer from the abiotic MeHg reservoir may be key, and are susceptible to ecological, climatic and biogeochemical influences. Deep and sustained cuts to global anthropogenic Hg emissions are required to return biotic Hg levels to their natural state. However, because of mass inertia and the less dominant role of atmospheric inputs, the decline of seawater and biotic Hg concentrations in the Arctic Ocean will be more gradual than the rate of emission reduction and slower than in other oceans and freshwaters. Climate warming has likely already influenced Arctic Hg dynamics, with shrinking sea-ice cover one of the defining variables. Future warming will probably force more Hg out of the ocean’s euphotic zone through greater evasion to air and faster Hg sedimentation driven by higher primary productivity; these losses will be countered by enhanced inputs from coastal erosion and rivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".