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Record W2112853880 · doi:10.5194/acp-11-6273-2011

Natural and anthropogenic atmospheric mercury in the European Arctic: a fractionation study

2011· article· en· W2112853880 on OpenAlexaff
A. O. Steen, Torunn Berg, Ashu Dastoor, Dorothy Durnford, Ola Engelsen, Lars Robert Hole, Katrine Aspmo Pfaffhuber

Bibliographic record

VenueAtmospheric chemistry and physics · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersNorsk PolarinstituttNorges Forskningsråd
KeywordsMercury (programming language)ArcticEnvironmental scienceEnvironmental chemistryThe arcticElemental mercuryAtmospheric sciencesChemistryOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract. Gaseous elemental mercury (GEM) is converted to reactive gaseous mercury (RGM) during springtime Atmospheric Mercury Depletion Events (AMDE). This study reports the longest time series of GEM, RGM and particle-bound mercury (PHg) concentrations from a European Arctic site. From 27 April 2007 until 31 December 2008 composite GEM, RGM and PHg measurements were conducted in Ny-Ålesund (78° 54′ N, 11° 53′ E). The average concentrations of the complete dataset were 1.6 ± 0.3 ng m−3, 8 ± 13 pg m−3 and 8 ± 25 pg m−3 for GEM, RGM and PHg, respectively. For the complete dataset the atmospheric mercury distribution was 99 % GEM, whereas RGM and PHg constituted <1 %. The study revealed a seasonal distribution of GEM, RGM and PHg previously undiscovered in the Arctic. Increased concentrations of RGM were observed during the insolation period from March through August, while increased PHg concentrations occurred almost exclusively during the spring AMDE period in March and April. The elevated RGM concentrations suggest that atmospheric RGM deposition also occurs during the polar summer. RGM was suggested as the precursor for the PHg existence, but long range transportation of PHg has to be taken into consideration. Still there remain gaps in the knowledge of how RGM and PHg are related in the environment. RGM and PHg accounted for on average about 10 % of the depleted GEM during AMDEs. Although speculative, the fairly low RGM and PHg concentrations supported by the predominance of PHg with respect to RGM and no clear meteorological regime associated with these AMDEs would all suggest the events to be of non-local origin. With some exceptions, no clear meteorological regime was associated with the GEM, RGM and PHg concentrations throughout the year.

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)0.001
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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