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Record W2106926783 · doi:10.1071/en08002

A mass balance inventory of mercury in the Arctic Ocean

2008· article· en· W2106926783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Chemistry · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsAir CanadaUniversity of ManitobaEnvironment and Climate Change CanadaFisheries and Oceans CanadaGeological Survey of Canada
FundersUniversität HeidelbergArcticNetFisheries Joint Management Committee
KeywordsMercury (programming language)Environmental scienceBioaccumulationArcticBiogeochemical cycleAbiotic componentSeawaterTrophic levelEnvironmental chemistryFood chainOceanographyContext (archaeology)BiotaMethylmercuryEcologyChemistryBiologyGeology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.210
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it