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Record W4214922699 · doi:10.31223/x5t331

Earth’s geodynamic evolution constrained by 182W in Archean seawater

2022· preprint· en· W4214922699 on OpenAlexaboutno aff
Andrea Mundl‐Petermeier, Sebastian Viehmann, Jonas Tusch, Michael Bau, Carsten Münker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftAustrian Science FundEuropean Commission
KeywordsArcheanGeologyRadiogenic nuclideMantle (geology)GeochemistryHydrothermal circulationSeawaterCrustContinental crustBanded iron formationIsotopeEarly EarthMineralogyEarth sciencePaleontologyOceanography

Abstract

fetched live from OpenAlex

Radiogenic isotope systems are important geochemical tools to unravel geodynamic processes on Earth1. Applied to ancient marine chemical sediments such as banded iron formations (BIFs), the short-lived 182Hf-182W isotope system can serve as key instrument to decipher Earth’s geodynamic evolution. High-precision 182W isotope data of the 2.7 Ga old BIF from the Temagami Greenstone Belt, NE Canada, reveal distinct 182W differences in alternating Si-rich (7.9 ppm enrichment) and Fe-rich (5.3 ppm enrichment) bands reflecting variable flux of W from continental and hydrothermal mantle sources into ambient seawater, respectively. Greater 182W excesses in Si-rich layers relative to associated shales (5.9 ppm enrichment), representing regional upper continental crust composition, suggest that the Si-rich bands record the global rather than the local seawater 182W signature. The distinct intra-band differences highlight the potential of 182W isotope signatures in BIFs to simultaneously track the evolution of crust and upper mantle through deep time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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