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Record W4413351553 · doi:10.1038/s43247-025-02678-3

Carbon isotope perturbations are not primarily driven by volcanism during the Late Paleozoic Ice Age

2025· article· en· W4413351553 on OpenAlexaff
Luojing Wang, Dawei Lv, Zhihui Zhang, Stephen E. Grasby, John L. Isbell, Jun Shen, Jianghai Yang

Bibliographic record

VenueCommunications Earth & Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNational Natural Science Foundation of China
KeywordsVolcanismPaleozoicGeologyEarth sciencePaleontologyIsotopes of carbonIce ageCarbon fibersAstrobiologyIsotopePhysicsMaterials scienceGlacial period

Abstract

fetched live from OpenAlex

Abstract The late Paleozoic ice age (LPIA) was the longest-lived glaciation of the Phanerozoic, and its demise marks Earth’s only recorded transition from an icehouse to a greenhouse state since the occurrence of vascular plants and complex terrestrial ecosystem. While global volcanism has been widely considered a key driver of carbon cycle during this period, limited high-resolution records have constrained our understanding. Here, we use high-resolution carbon isotope and mercury records from the North China Craton, spanning the late Gzhelian to early Kungurian stages, to evaluate the relationship between carbon cycle perturbation and volcanism. We identify two negative carbon isotope excursions during the late Gzhelian and early Asselian, both coinciding with climate warming. Our data reveal a variable relationship between carbon cycle disturbances and mercury records, suggesting volcanism was not the only trigger. Instead, they may result from the superimposition of multiple mechanisms, including tundra conditions, methane release, or orbitally-paced climate changes.

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 score0.012
Threshold uncertainty score0.025

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 head0.009
GPT teacher head0.217
Teacher spread0.207 · 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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