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Record W4407315057 · doi:10.1130/g52607.1

Proliferation of oxygen oases in Mesoarchean oceans

2025· article· en· W4407315057 on OpenAlexaff
Hui Ye, Chang‐Zhi Wu, Xiaolei Wang, Tao Yang, Yue Guan, Xiuqing Yang, Weiduo Hao, Kurt O. Konhauser, Weiqiang Li

Bibliographic record

VenueGeology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyOceanographyEarth sciencePaleontology

Abstract

fetched live from OpenAlex

Abstract Oxygenic photosynthesis played an essential role in the accumulation of free oxygen (O2) at Earth’s surface, but questions persist regarding its evolutionary timeline. Manganese (Mn)-rich sedimentary rocks from the Mesoarchean Pongola Supergroup in South Africa have been invoked among the earliest evidence for O2-dependent Mn(II) oxidation and thus photosynthetic O2 production in oceans. However, as a singular suite of rocks, uncertainties persist about whether the evidence for O2 in the Pongola region has global implications. Here we report on another Mesoarchean Mn-rich iron formation in South China, dating back to ca. 2.88–2.80 Ga. The Dianzihe iron formation exhibits a positive correlation between Mn enrichment (MnO up to 7.08 wt%, Fe/Mn ratio down to 5.1) and negative δ56Fe values (−0.21 to −1.33‰; average = −0.91‰). This pattern requires oxygenated seawater (i.e., O2 > 10 μM) at least to the seafloor, allowing not only for the oxidation of Fe(II) and Mn(II) but also for the preservation of Fe(Mn) oxyhydroxides until post-depositional modifications. Based on our findings in China alongside the distribution of Mn-rich iron formations in South Africa, we posit that a global distribution of oxygen oases, driven by cyanobacterial O2 production, already existed in the Mesoarchean.

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 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 score0.069
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.008
GPT teacher head0.234
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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