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Record W3192293529 · doi:10.1128/mbio.00490-21

Microbial Diversity and Function in Shallow Subsurface Sediment and Oceanic Lithosphere of the Atlantis Massif

2021· article· en· W3192293529 on OpenAlexaff
Jacqueline Goordial, Timothy D’Angelo, Jessica Labonté, Nicole Poulton, Julia M. Brown, Ramūnas Stepanauskas, Gretchen L. Früh‐Green, Beth N. Orcutt

Bibliographic record

VenuemBio · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Guelph
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAlfred P. Sloan FoundationCenter for Dark Energy Biosphere InvestigationsUnited States Science Support ProgramJoint Genome InstituteNational Science Foundation
KeywordsGeologyMassifSedimentLithosphereEarth scienceDiversity (politics)Function (biology)OceanographyGeochemistryGeomorphologyPaleontologyBiology

Abstract

fetched live from OpenAlex

The subsurface rock beneath the ocean is one of the largest biospheres on Earth, and microorganisms within influence global-scale nutrient cycles. This biosphere is difficult to study, in part due to the low concentrations of microorganisms that inhabit the vast volume of the marine lithosphere. In spite of the global significance of this biosphere, little is currently known about the microbial ecology of such rock-associated microorganisms. This study describes the identity and genomic potential of microorganisms in the subsurface rock and sediment at the Atlantis Massif, an underwater mountain near the Mid-Atlantic Ridge. To enable our analyses, fluorescence-activated cell sorting (FACS) was used as a means to concentrate cells from low biomass environmental samples for genomic analyses. We found distinct rock-associated microorganisms and found that the capacity for microorganisms to utilize organic carbon was the most prevalent form of carbon cycling. We additionally identified a potential role for carbon monoxide metabolism in the subsurface.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.171
Teacher spread0.164 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

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