Microbial ecology of the deep terrestrial subsurface
Bibliographic record
Abstract
The terrestrial subsurface hosts microbial communities that, collectively, are predicted to comprise as many microbial cells as global surface soils. Although initially thought to be associated with deposited organic matter, deep subsurface microbial communities are supported by chemolithoautotrophic primary production, with hydrogen serving as an important source of electrons. Despite recent progress, relatively little is known about the deep terrestrial subsurface compared to more commonly studied environments. Understanding the composition of deep terrestrial subsurface microbial communities and the factors that influence them is of importance because of human-associated activities including long-term storage of used nuclear fuel, carbon capture, and storage of hydrogen for use as an energy vector. In addition to identifying deep subsurface microorganisms, recent research focuses on identifying the roles of microorganisms in subsurface communities, as well as elucidating myriad interactions-syntrophic, episymbiotic, and viral-that occur among community members. In recent years, entirely new groups of microorganisms (i.e. candidate phyla radiation bacteria and Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoloarchaeota, Nanoarchaeota archaea) have been discovered in deep terrestrial subsurface environments, suggesting that much remains unknown about this biosphere. This review explores the historical context for deep terrestrial subsurface microbial ecology and highlights recent discoveries that shape current ecological understanding of this poorly explored microbial habitat. Additionally, we highlight the need for multifaceted experimental approaches to observe phenomena such as cryptic cycles, complex interactions, and episymbiosis, which may not be apparent when using single approaches in isolation, but are nonetheless critical to advancing our understanding of this deep biosphere.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".