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Record W4366772971 · doi:10.1038/s43247-023-00789-3

Geodynamic oxidation of Archean terrestrial surfaces

2023· article· en· W4366772971 on OpenAlexaff
Xiao Wu, Jianxi Zhu, Hongping He, Haiyang Xian, Yiping Yang, Lingya Ma, Xiaoliang Liang, Xiaoju Lin, Shan Li, Kurt O. Konhauser, Yiliang Li

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationNational Science Fund for Distinguished Young ScholarsGuangzhou Institute of Geochemistry, Chinese Academy of SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsArcheanWeatheringGeochemistryFelsicGeologySedimentary depositional environmentEarth scienceMaficSilicateChemistryPaleontology

Abstract

fetched live from OpenAlex

Abstract One of the paradoxes of the Archean rock record is how evidence for oxidative continental weathering is plausible when the atmosphere lacked oxygen prior to the Great Oxidation Event ca. 2.5 billion years ago. We detected reactive oxygen species (ROS) production at various silicate-water interfaces in a simulated Archean atmosphere and calculated homolysis rates of silicon–oxygen bonds in these minerals. We find that ROS can be produced while silicates were eroded and transported by streams to depositional basins. The magnitude of ROS production increases from mafic- to felsic-silicates. During early continent formation, e.g ., the Kaapvaal and Pilbara basins in the Paleoarchean, the emplacement of granitoids and their subsequent exposure to physical weathering might have annually provided at least 1.73 × 10 8 mol O 2 before the biological O 2 production. Importantly, this process could have oxidatively mobilized redox-sensitive elements to seawater, increasing the availability of bioessential nutrient elements for the Archean biosphere.

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 score0.195
Threshold uncertainty score0.999

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)0.0010.001

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 head0.029
GPT teacher head0.226
Teacher spread0.197 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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