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Record W4388742326 · doi:10.1016/j.epsl.2023.118491

Bimodality in zircon oxygen isotopes and implications for crustal melting on the early Earth

2023· article· en· W4388742326 on OpenAlexaboutno aff
Christopher L. Kirkland, Tim Johnson, Jack Gillespie, Laure Martin, Kai Rankenburg, Jonas Kaempf, Chris Clark

Bibliographic record

VenueEarth and Planetary Science Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
FundersAustralian Research CouncilState Key Laboratory of Geological Processes and Mineral ResourcesChina University of GeosciencesChina University of Geosciences, Wuhan
KeywordsZirconGeologyFelsicCrustCratonGeochemistryCrustal recyclingMantle (geology)Earth scienceBimodalityδ18OContinental crustPaleontologyTectonicsStable isotope ratioMafic

Abstract

fetched live from OpenAlex

Zircons from the oldest dated felsic crust, the Acasta Gneiss Complex, Canada, provide key information that may help understand the generation of crust on our nascent planet. When screened to eliminate grains with secondary alteration by measuring relative hydration (Δ16O1H/16O), primary ≥ 3.99 Ga zircon cores show δ18O of 5.88 ± 0.15 ‰, at the extreme upper (heavy) range for mantle values. Another early (≥3.96 Ga) zircon component indicates distinctly different, primary light δ18O values (δ18O ≤ 4.5 ‰). This bimodality in ancient zircon oxygen isotopes implies partial melting of both deep (lower crustal) and shallower (near surface) source rocks, responsible for felsic crust production on the early Earth. A similar bimodality in zircon δ18O is recognised in data from other ancient cratons, albeit at different times. Although alternative (uniformitarian) interpretations may also satisfy the data, the tempo of this bimodality matches models of planetary high-energy impact flux, consistent with a fundamental role for bolide impacts in the formation of crustal nuclei on the early Earth.

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 score0.024
Threshold uncertainty score0.426

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 studies0.0000.000
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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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