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Record W2123264831 · doi:10.1128/aem.03364-14

Atmospheric Hydrogen Scavenging: from Enzymes to Ecosystems

2014· review· en· W2123264831 on OpenAlexaff
Chris Greening, Philippe Constant, Kiel Hards, Sergio E. Morales, John G. Oakeshott, Robyn J. Russell, Matthew C. Taylor, Michael Berney, Ralf Conrad, Gregory M. Cook

Bibliographic record

VenueApplied and Environmental Microbiology · 2014
Typereview
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsArmand Frappier MuseumInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsActinobacteriaThermostabilityHydrogenaseHydrogenScavengingBiologyEnvironmental chemistryChemistryEnzymeEcologyBiochemistryGene

Abstract

fetched live from OpenAlex

We have known for 40 years that soils can consume the trace amounts of molecular hydrogen (H2) found in the Earth’s atmosphere.This process is predicted to be the most significant term in the global hydrogen cycle. However, the organisms and enzymes responsible for this process were only recently identified. Pure culture experiments demonstrated that several species of Actinobacteria, including streptomycetes and mycobacteria, can couple the oxidation of atmospheric H2 to the reduction of ambient O2. A combination of genetic, biochemical, and phenotypic studies suggest that these organisms primarily use this fuel source to sustain electron input into the respiratory chain during energy starvation. This process is mediated by a specialized enzyme, the group 5 [NiFe]-hydrogenase, which is unusual for its high affinity, oxygen insensitivity, and thermostability. Atmospheric hydrogen scavenging is a particularly dependable mode of energy generation, given both the ubiquity of the substrate and the stress tolerance of its catalyst. This minireview summarizes the recent progress in understanding how and why certain organisms scavenge atmospheric H2. In addition, it provides insight into the wider significance of hydrogen scavenging in global H2 cycling and soil microbial ecology.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations112
Published2014
Admission routes1
Has abstractyes

Explore more

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