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Record W1977488755 · doi:10.1051/jp4:20030239

Behavior of mercury in snow from different latitudes

2003· article· en· W1977488755 on OpenAlexafffund
Marc Amyot, Janick D. Lalonde, Parisa A. Ariya, Ashu Dastoor

Bibliographic record

VenueJournal de Physique IV (Proceedings) · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMcGill UniversityEnvironment and Climate Change CanadaInstitut National de la Recherche ScientifiqueUniversité de MontréalCégep Marie-Victorin
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsMercury (programming language)SnowLatitudeEnvironmental scienceAtmospheric sciencesEnvironmental chemistryGeologyMeteorologyChemistryGeographyGeodesyComputer science

Abstract

fetched live from OpenAlex

Deposition of Hg via snow fall may represent an important Hg flux to terrestrial and aquatic ecosystem of temperate and polar regions. We have conducted a series of field and laboratory experiments to better understand the post-depositional behaviour of Hg in snow. We found that: 1) a significant portion of the snow-to-air Hg evasion results from photoreduction of Hg in snow; 2) this photoreduction is mainly driven by UV-B radiation; 3) this photoreduction can be observed even in the presence of halogens in the snow, although we further found that these halogens favour the reverse photooxidation reaction; 4) laboratory experiments show that this photoreduction is likely mediated by organic reducing agents found in the snow matrix. These data will be incorporated in a global/regional Hg model. We propose that Hg falling inland in temperate and Arctic areas could be more rapidly re-emitted than Hg falling in coastal Arctic areas (where mercury depletion events occur).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations8
Published2003
Admission routes2
Has abstractyes

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