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Record W2991583730 · doi:10.1126/sciadv.aav2869

Photoferrotrophy, deposition of banded iron formations, and methane production in Archean oceans

2019· article· en· W2991583730 on OpenAlexafffund
Katharine J. Thompson, Paul A. Kenward, Kohen W. Bauer, Tyler J. Warchola, Tina Gauger, Raul E. Martinez, Rachel L. Simister, Céline Michiels, Marc Llirós, Christopher T. Reinhard, Andreas Kappler, Kurt O. Konhauser, Sean A. Crowe

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsArcheanDeposition (geology)MethaneBanded iron formationGeologyGeochemistryEnvironmental scienceEarth sciencePaleontologyBiologyEcologySediment

Abstract

fetched live from OpenAlex

Banded iron formation (BIF) deposition was the likely result of oxidation of ferrous iron in seawater by either oxygenic photosynthesis or iron-dependent anoxygenic photosynthesis-photoferrotrophy. BIF deposition, however, remains enigmatic because the photosynthetic biomass produced during iron oxidation is conspicuously absent from BIFs. We have addressed this enigma through experiments with photosynthetic bacteria and modeling of biogeochemical cycling in the Archean oceans. Our experiments reveal that, in the presence of silica, photoferrotroph cell surfaces repel iron (oxyhydr)oxides. In silica-rich Precambrian seawater, this repulsion would separate biomass from ferric iron and would lead to large-scale deposition of BIFs lean in organic matter. Excess biomass not deposited with BIF would have deposited in coastal sediments, formed organic-rich shales, and fueled microbial methanogenesis. As a result, the deposition of BIFs by photoferrotrophs would have contributed fluxes of methane to the atmosphere and thus helped to stabilize Earth's climate under a dim early Sun.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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 designBench or experimental
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

Citations73
Published2019
Admission routes2
Has abstractyes

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