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Record W1994549497 · doi:10.1021/es902186s

Historical Interrelated Variations of Mercury and Aquatic Organic Matter in Lake Sediment Cores from a Subarctic Lake in Yukon, Canada: Further Evidence toward the Algal-Mercury Scavenging Hypothesis

2009· article· en· W1994549497 on OpenAlexaffabout
Gary A. Stern, Hamed Sanei, Pat Roach, J. DeLaronde, P.M. Outridge

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsMercury (programming language)Subarctic climateScavengingSedimentOrganic matterEnvironmental sciencePaleolimnologyOceanographyMethylmercuryAquatic ecosystemEnvironmental chemistryEcologyGeologyChemistryBiologyGeomorphologyBioaccumulation

Abstract

fetched live from OpenAlex

This study investigates the role of climate-driven aquatic productivity increases in conrolling the sedimentary accumulation of mercury (Hg) in Kusawa Lake, Yukon, Canada. Organic geochemistry data reveals a significant link between the increasing trends of Hg and of labile algal-derived organic matter (OM) over the centuries and in particular over recent decades. Sedimentary Hg accumulation rates continue to increase even though atmospheric concentrations of Hg have remained the same or even declined. Together, these findings indicate that the significant rise in Hg content recorded in the sediments since the early 1950s may not be representative of increasing atmospheric Hg levels but are strongly influenced by elevated concentrations of algal-derived OM in the water-column. These likely increase the scavenging rate of "available" Hg from the water column. Therefore, recent increases in algal productivity may result in overestimation of the historical rates of atmospheric Hg deposition recorded in the sediments. This study confirms earlier published findings from two High Arctic lakes, and shows that the Hg-OM scavenging hypothesis may also apply to sub-Arctic lakes.

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.

How this classification was reachedexpand

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 score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations98
Published2009
Admission routes2
Has abstractyes

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