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Record W2043653426 · doi:10.1144/0016-76492009-161

Extreme sulphur isotope fractionation in the deep Cretaceous biosphere

2010· article· en· W2043653426 on OpenAlexaff
Vincenzo Ferrini, Mostafa Fayek, Caterina De Vito, Silvano Mignardi, Johannes Pıgnatti

Bibliographic record

VenueJournal of the Geological Society · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGeologyCretaceousBiosphereEarth scienceFractionationIsotopePaleontologyStable isotope ratioGeochemistryEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract: Isotopically light sulphur in sedimentary pyrite was generally predominant during the Cretaceous as a consequence of volcanic and hydrothermal activity and bacterial sulphate reduction (BSR). However, we report super-high sulphur isotopic compositions of sedimentary pyrite (δ 34 S +89.3‰) from late mid-Cretaceous sediments from the Central Apennines, Italy. These exceptionally high δ 34 S values are not consistent with current models for interpreting the geological record for sulphur isotopes because these cannot explain the extreme isotopic fractionation observed in our study. Precise details of the mechanism for producing pyrite so highly enriched in 34 S remain elusive, but the large variation in δ 34 S values (>70‰) within the analysed samples and the considerable micro-scale variations in δ 34 S (up to 50‰ even within a single nodule) indicate a complex mechanism of sulphate reduction and pyrite precipitation. This includes early removal of isotopically light H 2 S by pyrite precipitation in the water column, diffusion and advection of H 2 S and SO 4 2− , and finally BSR. Similar but less severe conditions occur in the Black Sea and in deep ocean sediments. These new findings provide new insights for the interpretation of the palaeoceanographic conditions that prevailed during the Mesozoic.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

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