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Record W4404420811 · doi:10.1093/nsr/nwae414

Influence of global warming and human activity on mercury accumulation patterns in wetlands across the Qinghai–Tibet Plateau

2024· article· en· W4404420811 on OpenAlexaff
Xinbin Feng, Xun Wang, Longyu Jia, Wei Yuan, Lu Meng, Nantao Liu, Fei Wu, Xinyuan Cai, Fei Wang, Che-Jen Lin

Bibliographic record

VenueNational Science Review · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsWetlandMercury (programming language)Plateau (mathematics)Environmental scienceQinghai lakeGlobal warmingPhysical geographyClimatologyClimate changeAtmospheric sciencesEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Wetlands in the Qinghai-Tibet Plateau are a unique and fragile ecosystem undergoing rapid changes. We show two unique patterns of mercury (Hg) accumulation in wetland sediments. One is the 'surface peak' in monsoon-controlled regions and the other is the 'subsurface peak' in westerly-controlled regions. The former is attributed to the combined effects of increasing anthropogenic emissions and climate-induced changes in the cryosphere and wetland hydrology in the last 100-150 years. The climate changes in westerly-controlled regions in the last 50-70 years led to a fluctuation in hydrology and Hg peak in the sediment subsurface. The increase in legacy Hg input from soil erosion has largely enhanced the Hg accumulation rate in wetlands since the 1950s, especially in the proglacial wetlands. We highlight that accelerated glacier melting and permafrost thawing caused by global warming have altered geomorphology and hydrology, and affected Hg transport and accumulation in wetlands.

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 score0.087
Threshold uncertainty score0.173

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 studies0.0000.000
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 head0.049
GPT teacher head0.412
Teacher spread0.362 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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