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Record W4406805096 · doi:10.1029/2024gl113112

Mercury Isotopes Track the Causes of Carbon Perturbations in the Early Permian Ocean and Continent

2025· article· en· W4406805096 on OpenAlexaff
Qiang Fang, Huaichun Wu, Jiubin Chen, Stephen E. Grasby, Wang Zheng, Shu‐zhong Shen, Wentao Huang, Junjie Xu, Shihong Zhang

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNational Natural Science Foundation of China
KeywordsMercury (programming language)IsotopeIsotopes of carbonGeologyPermianEnvironmental scienceOceanographyEarth sciencePaleontologyNuclear physics

Abstract

fetched live from OpenAlex

Abstract The Early Permian witnessed the first icehouse‐to‐greenhouse turnover of the vegetated Earth, yet its climate dynamics remain enigmatic. Here, we used mercury (Hg) isotopes from pelagic and continental successions at low paleo‐latitudes to track the perturbations of the global carbon (C) cycle and the climatic impact. Our results indicate that small‐scale volcanism promoted marine organic C burial, and the concomitant extreme cooling triggered the waning of wetland ecosystems in North China block at ∼296.2 Ma. Subsequently, the mass‐independent fractionation of odd Hg isotopes (Δ 199 Hg) and C isotopes synchronously decline in the deep‐marine succession, likely supporting progressive oxidation of terrestrial biomass and airborne release of Hg and C. Lowered C sequestration (as coal swamps) on land and dampened continental weathering limited the drawdown of CO 2 emissions from wildfires, initiating deglaciation. Our findings highlight that the climate forcing on terrestrial ecosystems could activate additional C reservoirs, driving Earth into a warmer state.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.318
Teacher spread0.287 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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