Atmospheric Hydrogen Scavenging: from Enzymes to Ecosystems
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".