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Record W3034706908 · doi:10.1073/pnas.1921042117

A seawater throttle on H <sub>2</sub> production in Precambrian serpentinizing systems

2020· article· en· W3034706908 on OpenAlexafffund
Benjamin M. Tutolo, William E. Seyfried, Nicholas J. Tosca

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsPrecambrianSeawaterRedoxEarth (classical element)Early EarthAtmosphere (unit)Production (economics)AstrobiologyChemistryMineralogyGeologyEnvironmental scienceEnvironmental chemistryEarth scienceGeochemistryThermodynamicsInorganic chemistryPhysicsOceanography

Abstract

fetched live from OpenAlex

Significance Our current understanding of the redox state of Earth’s ancient atmosphere is heavily dependent on the assumption that ancient low-temperature serpentinizing systems produced abundant H 2 , as they do today. Here, we examine this assumption using data that constrain Fe partitioning in serpentine minerals. We find that H 2 production is linked to a Si deficiency in the serpentine structure, which itself is caused by low SiO 2 (aq) concentrations in fluids derived from modern seawater. Calculations accounting for this dependence of H 2 production on seawater SiO 2 (aq) imply that Precambrian serpentinizing systems would have produced up to about two orders of magnitude less H 2 than today, prompting a reexamination of atmospheric redox state and H 2 -dependent origins of life scenarios on 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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.060
GPT teacher head0.274
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations57
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of Sciences→Same topicGeology and Paleoclimatology Research→French-language works237,207→