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Record W2552635721 · doi:10.1021/acs.est.6b04276

The Estimated Six-Year Mercury Dry Deposition Across North America

2016· article· en· W2552635721 on OpenAlexaff
Leiming Zhang, Zhiyong Wu, Irene Cheng, L. Paige Wright, Mark L. Olson, David A. Gay, Martin R. Risch, Steven B. Brooks, Mark S. Castro, Gary D. Conley, Eric S. Edgerton, Thomas M. Holsen, Winston T. Luke, Robert Tordon, P. S. Weiss‐Penzias

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsMercury (programming language)Environmental scienceEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Dry deposition of atmospheric mercury (Hg) to various land covers surrounding 24 sites in North America was estimated for the years 2009 to 2014. Depending on location, multiyear mean annual Hg dry deposition was estimated to range from 5.1 to 23.8 μg m –2 yr –1 to forested canopies, 2.6 to 20.8 μg m –2 yr –1 to nonforest vegetated canopies, 2.4 to 11.2 μg m –2 yr –1 to urban and built up land covers, and 1.0 to 3.2 μg m –2 yr –1 to water surfaces. In the rural or remote environment in North America, annual Hg dry deposition to vegetated surfaces is dominated by leaf uptake of gaseous elemental mercury (GEM), contrary to what was commonly assumed in earlier studies which frequently omitted GEM dry deposition as an important process. Dry deposition exceeded wet deposition by a large margin in all of the seasons except in the summer at the majority of the sites. GEM dry deposition over vegetated surfaces will not decrease at the same pace, and sometimes may even increase with decreasing anthropogenic emissions, suggesting that Hg emission reductions should be a long-term policy sustained by global cooperation.

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 categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
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 studies0.0020.009
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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

Citations84
Published2016
Admission routes1
Has abstractyes

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