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Record W2100153488 · doi:10.1038/ncomms1217

Multiple S-isotopic evidence for episodic shoaling of anoxic water during Late Permian mass extinction

2011· article· en· W2100153488 on OpenAlexafffund
Yanan Shen, James Farquhar, Hua Zhang, Andrew L. Masterson, Tonggang Zhang, Boswell A. Wing

Bibliographic record

VenueNature Communications · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaJohn Simon Guggenheim Memorial Foundation
KeywordsPermian–Triassic extinction eventAnoxic watersExtinction eventExtinction (optical mineralogy)BioturbationGeologyPermianOceanographySedimentary rockShoaling and schoolingPhanerozoicPaleontologyWater massBiodiversityEnvironmental scienceEcologySedimentBiologyCenozoic

Abstract

fetched live from OpenAlex

Global fossil data show that profound biodiversity loss preceded the final catastrophe that killed nearly 90% marine species on a global scale at the end of the Permian. Many hypotheses have been proposed to explain this extinction and yet still remain greatly debated. Here, we report analyses of all four sulphur isotopes (32S, 33S, 34S and 36S) for pyrites in sedimentary rocks from the Meishan section in South China. We observe a sulphur isotope signal (negative δ34S with negative Δ33S) that may have resulted from limitation of sulphate supply, which may be linked to a near shutdown of bioturbation during shoaling of anoxic water. These results indicate that episodic shoaling of anoxic water may have contributed to the profound biodiversity crisis before the final catastrophe. Our data suggest a prolonged deterioration of oceanic environments during the Late Permian mass extinction. A final catastrophe killed 90% of marine species at the end of the Permian period, but significant biodiversity loss preceded this event. In this study, sulphur isotope evidence suggests that incursion of anoxic water into shallow regions may have contributed to biodiversity loss.

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)0.000
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 score0.024
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.076
GPT teacher head0.288
Teacher spread0.213 · 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

Citations174
Published2011
Admission routes2
Has abstractyes

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