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Record W4391026640 · doi:10.1029/2023gl106654

Large Igneous Province Emplacement Triggered an Oxygenation Event at ∼1.4 Ga: Evidence From Mercury and Paleo‐Productivity Proxies

2024· article· en· W4391026640 on OpenAlexaff
Lei Xu, Maxwell Lechte, Xiaoying Shi, Wang Zheng, Limin Zhou, Kang‐Jun Huang, Xiqiang Zhou, Dongjie Tang

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsGeologyProterozoicWeatheringPyriteIgneous rockTrace elementTotal organic carbonGeochemistryPaleontologyEnvironmental chemistryChemistryTectonics

Abstract

fetched live from OpenAlex

Abstract The mid‐Proterozoic (∼1.8–0.8 Ga) ocean‐atmosphere system is hypothesized to have experienced fluctuations in redox conditions with transient oxygenation events. One of these happened at ∼1.4 Ga, and it is speculated that this event may link to the emplacement of large igneous province (LIP) at this time. However, direct evidence for this relationship remains to be proved. Here, we report Hg/TOC, P, and trace element concentrations across the ∼1.4 Ga oxygenation event in the Xiamaling Formation of North China. A prominent increase in Hg/TOC is slightly earlier than that of nutrient contents (especially P), pyrite and TOC abundances, suggesting that this distinct oxygenation event was likely the result of LIP activity at ∼1.4 Ga, which increased nutrient and sulfate supply from continental weathering to the ocean, sustaining elevated primary productivity, organic carbon and pyrite burial. This study indicates that LIP weathering could trigger transient oxygenation events during the mid‐Proterozoic.

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 score0.024
Threshold uncertainty score0.047

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)0.0010.000

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 head0.045
GPT teacher head0.318
Teacher spread0.273 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

Explore more

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