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Record W1994253414 · doi:10.1021/es100724y

Natural and Anthropogenic Mercury Distribution in Marine Sediments from Hudson Bay, Canada

2010· article· en· W1994253414 on OpenAlexaffabout
Alexander A. Hare, Gary A. Stern, Zou Zou A. Kuzyk, Robie W. Macdonald, Sophia C. Johannessen, Fei Wang

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
Fundersnot available
KeywordsBayMercury (programming language)Sedimentary rockSedimentSedimentary organic matterOceanographyDeposition (geology)Environmental scienceEnvironmental chemistryArcticEstuaryOrganic matterGeologyGeochemistryEcologyChemistryGeomorphology

Abstract

fetched live from OpenAlex

Twelve marine sediment cores from Hudson Bay, Canada, were collected to investigate the response of sub-Arctic marine sediments to atmospherically transported anthropogenic mercury (Hg). Modeling by a two-layer sediment mixing model suggests that the historical Hg deposition to most of the sediment cores reflects the known history of atmospheric Hg deposition in North America, with an onset of increasing anthropogenic Hg emissions in the late 1800s and early 1900s and a reduction of Hg deposition in the mid- to late-1900s. However, although anthropogenic Hg has contributed to a ubiquitous increase in Hg concentrations in sediments over the industrial era, the most elevated industrial-era sedimentary Hg concentrations only marginally exceed the upper preindustrial sedimentary Hg concentrations. Analysis of delta13C and relationship between Hg and organic matter capture suggests that the response of Hudson Bay sediments to changes in atmospheric Hg emissions is largely controlled by the particle flux in the system and that natural changes in organic matter composition and dynamics can cause variation in sedimentary Hg concentrations at least to the same extent as those caused by increasing anthropogenic Hg emissions.

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 categoriesScience and technology studies, Insufficient 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 score0.311
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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.003
GPT teacher head0.213
Teacher spread0.209 · 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

Citations50
Published2010
Admission routes2
Has abstractyes

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