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Record W4415168196 · doi:10.1144/jgs2025-088

Sulfate reduction and hydrogen sulfide oxidation rates in marine sediments with cryptic sulfur cycling

2025· article· en· W4415168196 on OpenAlexaff
Tomas Israel Grijalva-Rodriguez, Gilad Antler, Efrat Eliani-Russak, Dong Feng, Shanggui Gong, André Pellerin, Hans Røy, Keren Solomovich, Alexandra V. Turchyn, Irina Zweig, Alexey Kamyshny

Bibliographic record

VenueJournal of the Geological Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversité du Québec à Rimouski
FundersIsrael Science Foundation
KeywordsSulfateSulfurHydrogen sulfideSulfur cycleSulfidePyriteCycling

Abstract

fetched live from OpenAlex

Sedimentary systems affected by high fluxes of reactive iron develop a so-called ‘cryptic’ sulfur cycle in which hydrogen sulfide is nearly fully reoxidized and its concentrations of hydrogen sulfide in the porewaters are in the submicromolar range. The sediments of the Gulf of Aqaba represent a classic example of such a system. The goal of this work was to provide quantitative constraints on hydrogen sulfide concentrations in the sediments of the Gulf of Aqaba. Sulfate reduction rates in the sediments of the Gulf of Aqaba were found to be lower than in marine sediments that had not been affected by high fluxes of the reactive iron, while the rate constants of hydrogen sulfide oxidation were found to be higher than in the highly reactive iron-rich sediments of the Svalbard fjord. A combination of slow rates of sulfate reduction and fast rates of hydrogen sulfide oxidation results in concentrations of hydrogen sulfide in the porewater, both measured and calculated, that are below 100 nmol l −1 . We suggest that a similar cycling of sulfur species may occur in organic-matter-poor marine systems situated in dry environments with highly reactive iron mineral delivery, such as the Red Sea and the Atlantic Ocean in the vicinity of the Sahara.

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 score0.019
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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