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Record W2059570941 · doi:10.1021/es300166x

Mercury Distribution and Deposition in Glacier Snow over Western China

2012· article· en· W2059570941 on OpenAlexaff
Qianggong Zhang, Jie Huang, Fei Wang, Loewen Mark, Jianzhong Xu, Debbie Armstrong, Chaoliu Li, Yulan Zhang, Shichang Kang

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGlacierSnowMeltwaterMercury (programming language)ParticulatesPhysical geographyAccumulation zoneDeposition (geology)Environmental scienceAtmospheric sciencesIce coreGeologyCryosphereClimatologyGeomorphologyGeographySedimentSea iceChemistryIce stream

Abstract

fetched live from OpenAlex

Western China is home to the largest aggregate of glaciers outside the polar regions, yet little is known about how the glaciers in this area affect the transport and cycling of mercury (Hg) regionally and globally. From 2005 to 2010, extensive glacier snow sampling campaigns were carried out in 14 snowpits from 9 glaciers over western China, and the vertical distribution profiles of Hg were obtained. The Total Hg (THg) concentrations in the glacier snow ranged from <1 to 43.6 ng L(-1), and exhibited clear seasonal variations with lower values in summer than in winter. Spatially, higher THg concentrations were typically observed in glacier snows from the northern region where atmospheric particulate loading is comparably high. Glacier snowpit Hg was largely dependent on particulate matters and was associated with particulate Hg, which is less prone to postdepositional changes, thus providing a valuable record of atmospheric Hg deposition. Estimated atmospheric Hg depositional fluxes ranged from 0.74 to 7.89 μg m(-2) yr(-1), agreeing very well with the global natural values, but are one to two orders of magnitude lower than that of the neighboring East Asia. Elevated Hg concentrations were observed in refrozen ice layers in several snowpits subjected to intense melt, indicating that Hg can be potentially released to meltwater.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.771

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 studies0.0000.002
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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.

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

Citations126
Published2012
Admission routes1
Has abstractyes

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