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Record W2915824663 · doi:10.1128/msystems.00091-18

Contrasting Pathways for Anaerobic Methane Oxidation in Gulf of Mexico Cold Seep Sediments

2019· article· en· W2915824663 on OpenAlexaff
Adrien Vigneron, Eric Alsop, Perrine Cruaud, Gwenaelle Philibert, Benjamin L. King, Leslie Baksmaty, David Lavallée, B. P. Lomans, Emiley A. Eloe‐Fadrosh, Nikos C. Kyrpides, Ian M. Head, Nicolas Tsesmetzis

Bibliographic record

VenuemSystems · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversité Laval
FundersOffice of ScienceResearch Councils UKSight Research UKJoint Genome InstituteNatural Environment Research CouncilShellShell Global Solutions InternationalU.S. Department of Energy
KeywordsCold seepAnaerobic oxidation of methanePetroleum seepMethaneOceanographyAnaerobic exerciseGeologyEnvironmental scienceGeochemistryEcologyBiology

Abstract

fetched live from OpenAlex

Cold seep sediments are complex and widespread marine ecosystems emitting large amounts of methane, a potent greenhouse gas, and other hydrocarbons. Within these sediments, microbial communities play crucial roles in production and degradation of hydrocarbons, modulating oil and gas emissions to seawater. Despite this ecological importance, our understanding of microbial functions and methane oxidation pathways in cold seep ecosystems is poor. Based on gene expression profiling of environmental seep sediment samples, the present work showed that (i) the composition of the emitted fluids shapes the microbial community in general and the anaerobic methanotroph community specifically and (ii) AOM by ANME-2 in this seep may be coupled to sulfate reduction by Deltaproteobacteria by electron transfer through multiheme cytochromes, whereas AOM by ANME-1 lineages in this seep may involve a different, bacterium-independent pathway, coupling methane oxidation to elemental sulfur/polysulfide reduction.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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 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

Citations46
Published2019
Admission routes1
Has abstractyes

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