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Record W1987667560 · doi:10.1021/es101991p

Increased Accumulation of Sulfur in Lake Sediments of the High Arctic

2010· article· en· W1987667560 on OpenAlexaffabout
Paul E. Drevnick, Derek C. G. Muir, Carl H. Lamborg, Martin J. Horgan, Donald E. Canfield, Neil L. Rose

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsInstitut National de la Recherche ScientifiqueEnvironment and Climate Change Canada
Fundersnot available
KeywordsSulfateSulfurBiogeochemical cycleSulfur cycleSedimentEnvironmental chemistryArcticSulfideSedimentationEnvironmental scienceDetritusDimethyl sulfideOceanographyChemistryGeologyGeomorphology

Abstract

fetched live from OpenAlex

We report a synchronous increase in accumulation of reduced inorganic sulfur since c. 1980 in sediment cores from eight of nine lakes studied in the Canadian Arctic and Svalbard (Norway). Sediment incubations and detailed analyses of sediment profiles from two of the lakes indicate that increases in sulfur accumulation may be due ultimately to a changing climate. Warming-induced lengthening of the ice-free season is resulting in well-documented increases in algal production and sedimentation of the resulting detrital matter. Algal detritus is a rich source of labile carbon, which in these sediments stimulates dissimilatory sulfate reduction. The sulfide produced is stored in sediment (as acid volatile sulfide), converted to other forms of sulfur, or reoxidized to sulfate and lost to the water column. An acceleration of the sulfur cycle in Arctic lakes could have profound effects on important biogeochemical processes, such as carbon burial and mercury methylation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.260
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2010
Admission routes2
Has abstractyes

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