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Record W4231284256 · doi:10.46427/gold2020.314

Mercury Methylation in Permafrost Thaw Ecosystems

2020· article· en· W4231284256 on OpenAlexaffabout
João Canário, Martin Jusek, Holger Hintelmann, Martin Pilote Pilote, Gustaf Hugelius, Julia Wagner, Gonçalo Vieira, V. J. Martin, Andreas Ritcher, Rachele Lodi, Hugues Lantuit

Bibliographic record

VenueGoldschmidt Abstracts · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsTrent UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsPermafrostMercury (programming language)EcosystemEnvironmental scienceEnvironmental chemistryEarth scienceAstrobiologyGeologyEcologyComputer scienceOceanographyChemistryBiology

Abstract

fetched live from OpenAlex

Arctic permafrost contains twice the amount of mercury (Hg) present in the world ocean, atmosphere and soils combined [1]. This Hg can potentially be remobilized during permafrost thaw, methylated and released to ecosystems. To identify and quantify Hg and methylmercury (MeHg) levels and their transformation rates, permafrost soils, thaw lake waters and sediments were sampled in the Canadian Arctic and analyzed for Hg and MeHg content. Mercury methylation and MeHg demethylation rates were also calculated using Hg stable isotope techniques.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.476

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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