Rock-Hosted Subsurface Biofilms: Mineral Selectivity Drives Hotspots for Intraterrestrial Life
Bibliographic record
Abstract
The continental deep subsurface is likely the largest reservoir of biofilm-based microbial biomass on Earth, but the role of mineral selectivity in regulating its distribution and diversity is unclear. Minerals can produce hotspots for intraterrestrial life by locally enhancing biofilm biomass. Metabolic transformations of minerals by subsurface biofilms may occur widely with the potential to significantly impact subsurface biogeochemical cycles. However, the degree of impact depends upon the amount of biofilm biomass and its relationship to host rock mineralogy, estimates that are currently loosely constrained to non-existent. Here, we use in situ cultivation of biofilms on native rocks and coupled microscopy/spectroscopy to constrain mineral selectivity by biofilms in a deep continental subsurface setting: the Deep Mine Microbial Observatory (DeMMO). Through hotspot analysis and spatial modeling approaches we find that mineral distributions, particularly those putatively metabolized by microbes, indeed drive biofilm distribution at DeMMO, and that bioleaching of pyrite may be a volumetrically important process influencing fluid geochemistry at this site when considered at the kilometer scale. Given the ubiquity of iron-bearing minerals at this site and globally, and the amount of biomass they can support, we posit that rock-hosted biofilms likely contribute significantly to subsurface biogeochemical cycles. As more data becomes available, future efforts to estimate biomass in the continental subsurface should incorporate host rock mineralogy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".