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Record W2912089536 · doi:10.1080/00206814.2019.1571448

The contribution of the Emeishan large igneous province to the strontium isotope evolution of the Capitanian seawater

2019· article· en· W2912089536 on OpenAlexaff
Hu Huang, Mingcai Hou, Hairuo Qing, Lian Zhou, Jianghai Yang, Yuansheng Du, Jingchun Tian, Shijun Ni, Fuhao Xiong

Bibliographic record

VenueInternational Geology Review · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Regina
FundersChengdu University of TechnologyChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsLarge igneous provinceBasaltGeologyIsotopes of strontiumSeawaterStrontiumGeochemistryIgneous rockPaleontologyMagmatismPhysicsOceanographyNuclear physicsTectonics

Abstract

fetched live from OpenAlex

The 87Sr/86Sr minimum of the Capitanian seawater is one of the most significant features in the Phanerozoic seawater 87Sr/86Sr history. In order to assess possible contribution of the Emeishan large igneous provinces (LIPs) to strontium isotope evolution of the Capitanian seawater, 87Sr/86Sr ratios were measured from the Capitanian limestones which are locally interlayered with the Emeishan basalts. The limestones underlying the Emeishan basalts have high 87Sr/86Sr ratios (0.7070–0.7074). However, extremely low 87Sr/86Sr ratios (0.7068–0.7070) were identified in the late Capitanian Jinogondolella prexuanhanensis–J. xuanhanensis zones, which correspond to the eruption time of the Emeishan LIP. The temporal coincidence of these two phenomena supports the idea of a potential linkage between Capitanian 87Sr/86Sr minimum and eruption of this igneous province. The strong submarine hydrothermal activity and erosion of the Emeishan LIP could have released large amounts of non-radiogenic Sr to the oceans and play an important role in strontium isotope evolution of the seawater.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.194
Teacher spread0.191 · 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 teacher head, not a consensus.

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

Citations11
Published2019
Admission routes1
Has abstractyes

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