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Record W2790045453 · doi:10.1002/2017gl076311

Low‐Fe(III) Greenalite Was a Primary Mineral From Neoarchean Oceans

2018· article· en· W2790045453 on OpenAlexfundno aff
Jena E. Johnson, Janet R. Muhling, Julie Cosmidis, Alexis S. Templeton

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
FundersNational Research Council CanadaCanadian Institutes of Health ResearchAgouron InstituteUniversity of Saskatchewan
KeywordsBanded iron formationMineralGeologyBiogeochemistryPrimary (astronomy)GeochemistryEarth scienceMineralogyOceanographyArcheanMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract Banded iron formations (BIFs) represent chemical precipitation from Earth's early oceans and therefore contain insights into ancient marine biogeochemistry. However, BIFs have undergone multiple episodes of alteration, making it difficult to assess the primary mineral assemblage. Nanoscale mineral inclusions from 2.5 billion year old BIFs and ferruginous cherts provide new evidence that iron silicates were primary minerals deposited from the Neoarchean ocean, contrasting sharply with current models for BIF inception. Here we used multiscale imaging and spectroscopic techniques to characterize the best preserved examples of these inclusions. Our integrated results demonstrate that these early minerals were low‐Fe(III) greenalite. We present potential pathways in which low‐Fe(III) greenalite could have formed through changes in saturation state and/or iron oxidation and reduction. Future constraints for ancient ocean chemistry and early life's activities should include low‐Fe(III) greenalite as a primary mineral in the Neoarchean ocean.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.285
Teacher spread0.251 · 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

Citations76
Published2018
Admission routes1
Has abstractyes

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