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Record W4386474235 · doi:10.1126/sciadv.adh9502

Deglacial volcanism and reoxygenation in the aftermath of the Sturtian Snowball Earth

2023· article· en· W4386474235 on OpenAlexaff
Menghan Li, Yilun Xu, Lilin Sun, Jiubin Chen, Ke Zhang, Dandan Li, James Farquhar, Xiaolin Zhang, Ruoyu Sun, Francis A. Macdonald, Stephen E. Grasby, Yong Fu, Yanan Shen

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNational Postdoctoral Program for Innovative TalentsHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsSnowball EarthGeologyVolcanismAstrobiologySupercontinentEarth scienceDeglaciationPaleontologyGeochemistryGlacial periodTectonics

Abstract

fetched live from OpenAlex

The Cryogenian Sturtian and Marinoan Snowball Earth glaciations bracket a nonglacial interval during which Demosponge and green-algal biomarkers first appear. To understand the relationships between environmental perturbations and early animal evolution, we measured sulfur and mercury isotopes from the Datangpo Formation from South China. Hg enrichment with positive Δ 199 Hg excursion suggests enhanced volcanism, potentially due to depressurization of terrestrial magma chambers during deglaciation. A thick stratigraphic interval of negative Δ 33 S py indicates that the nonglacial interlude was characterized by low but rising sulfate levels. Model results reveal a mechanism to produce the Δ 33 S anomalies down to −0.284‰ through Rayleigh distillation. We propose that extreme temperatures and anoxia contributed to the apparent delay in green algal production in the aftermath of the Sturtian glaciation and the subsequent reoxygenation of the iron-rich and sulfate-depleted ocean paved the way for evolution of animals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.266
Teacher spread0.252 · 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.

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

Citations21
Published2023
Admission routes1
Has abstractyes

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