Molecular evidence for an active endogenous microbiome beneath glacial ice
Bibliographic record
Abstract
Geologic, chemical and isotopic evidence indicate that Earth has experienced numerous intervals of widespread glaciation throughout its history, with roughly 11% of present day Earth's land surface covered in ice. Despite the pervasive nature of glacial ice both today and in Earth's past and the potential contribution of these systems to global biogeochemical cycles, the composition and phylogenetic structure of an active microbial community in subglacial systems has yet to be described. Here, using RNA-based approaches, we demonstrate the presence of active and endogenous archaeal, bacterial and eukaryal assemblages in cold (0-1 °C) subglacial sediments sampled from Robertson Glacier, Alberta, Canada. Patterns in the phylogenetic structure and composition of subglacial sediment small subunit (SSU) ribosomal RNA (rRNA) assemblages indicate greater diversity and evenness than in glacial surface environments, possibly due to facilitative or competitive interactions among populations in the subglacial environment. The combination of phylogenetically more even and more diverse assemblages in the subglacial environment suggests minimal niche overlap and optimization to capture a wider spectrum of the limited nutrients and chemical energy made available from weathering of bedrock minerals. The prevalence of SSU rRNA affiliated with lithoautotrophic bacteria, autotrophic methane producing archaea and heterotrophic eukarya in the subglacial environment is consistent with this hypothesis and suggests an active contribution to the global carbon cycle. Collectively, our findings demonstrate that subglacial environments harbor endogenous active ecosystems that have the potential to impact global biogeochemical cycles over extended periods of time.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".