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Record W2372349408

Biogeochemistry of Organic Acids in Snow and Ice: A Review

2001· review· en· W2372349408 on OpenAlexaboutno aff
Xin Li, Xinqing Lee

Bibliographic record

VenueJournal of Glaciolgy and Geocryology · 2001
Typereview
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSnowFirnBiogeochemistryGlacierBiogeochemical cycleEnvironmental chemistryCryosphereChemistryIce coreTotal organic carbonOrganic matterEnvironmental sciencePhysical geographyEarth scienceSea iceGeologyOceanographyOrganic chemistryGeography
DOInot available

Abstract

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The study of organic acids recorded in snow and ice provides a unique way to obtain information on a variety of the species in response topast climate change,past environment and past ecosystem, and represents one of the main fields in glaciochemistry. It also provides insight into biogeochemical cycles of carbon, hydrogen and oxygen and other major biological elements. In August 1987, a special session on organic acids and related compounds in the atmosphere was held at the Sixth International Symposium of the Commission on Atmospheric Chemistry and Global Pollution in Peterborough, Canada, which symbolized that studies of organic acids had drawn attention of researchers around the world. The organic acids detected in snow and ice are formic, acetic, propionic, pyruvic, oxalic and glycolic. The former two represent the most abundant ones. In some places, their concentrations are even higher than inorganic acids such as sulfate, nitrate and chloride. For the past decade, the study of the organic acids mainly focused on Greenland and Antarctica. Recent two years, however, the study turns to alpine glaciers in the middle and low latitude areas because organic acids recorded in alpine glaciers are much closer to the sources than those in polar areas. Studies show that formic and acetic in Greenland ice cores reached up to 10ng·g -1 in their average concentrations with the former higher than the latter, while the oxalic and glycolic are below 1 ng·g -1 . They mainly came from biomass burning, which accounts for about 20 %, and vegetation emissions in the north hemisphere that contribute to the background of the organic acids. In even lower concentrations, the formic acids recorded in Antarctic ice cores are below 2 ng·g -1 , whereas the acetic below 0.15ng·g -1 . They are considered as from the atmospheric oxidation of numerous hydrocarbons such as methane and alkenes. MSA in Greenland ice cores is below 5ng·g -1 on an average, whereas that in Antarctica up 7ng·g -1 . They all came from the oceanic emission of DMS. In sharp contrast, formic and acetic acids in the Glacier No.1 at the headwaters of the rumqi River, a middle latitude alpine glacier in the Tianshan Mountains, West China, are several ten-fold higher than those in Greenland, and a few thousands times more than those in Antarctica. Moreover, resolution of organic acid records in middle latitude alpine glaciers is also higher than those in polar areas. This demonstrates that the organic records in alpine glaciers are more conducive to understanding the biogeochemical cycles of the organic acids. Climate changes affect the terrestrial vegetation, the source for the carboxylic acids, and the oceanic production of DMS, the precursor of MSA, thus the secular trends of the organic acid records in ice cores. It is interested to note that MSA connects the climate change in different ways for the two hemispheres. Greenland ice cores demonstrate the MSA correlates positively with climate change, while it does somewhat negatively in Antarctica. ENSO affects the production of DMS in the southern hemisphere and it probably causes the disparity. The change of the formic/acetic ratio in the Greenland ice core from 1945 suggests the anthropogenic impact from the northern hemisphere, which caused an increase of acetic acid while a decrease of formic.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2001
Admission routes1
Has abstractyes

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