Characterization of active microbial ecosystems in icy astrobiology analog cryo-environments.
Bibliographic record
Abstract
Ice environments are characterized by permanent subzero temperatures, low nutrient concentrations and low water activity, presenting unique challenges to microbial life.The study of microbes inhabiting ice informs our understanding of the cold limits of life on Earth and extraterrestrial bodies.Lava tube ice caves are analog environments to lava tubes identified on Mars which may contain microbial ecosystems.Glaciers and ice caps are analogs to the icy moons of Jupiter and Saturn which contain thick ice crusts hiding subsurface oceans and may also support a microbial ecosystem.Lava Beds National Monument, located next to the Medicine Lake Volcano in Northern California contains the largest concentration of lava tubes in North America but no study of the microbial ecology of the ice in these caves has been performed.The Devon Island ice cap, and White Glacier in the Canadian high Arctic and Johnsons Glacier in Antarctica are some of the best-studied ice masses in the polar regions, however, little if any information is known of the microbial communities which reside within them.Culture-dependent and independent methods were used to study the microbial ecosystems in all four of these environments.In Lava Beds National Monument, 16S rRNA amplicon sequencing identified ice samples primarily consisting of Actinomycetota, Pseudomonadota, Bacteroidota, Bacillota, and Chloroflexota and culture experiments and metabolic activity assays identified a viable and metabolically active microbial community at subzero temperatures.Furthermore, these communities were found to be more closely related to other cryoenvironments than caves suggesting that ice and cold temperatures exert stronger selection pressures on microbial communities than caves.In the Devon Island ice cap, flow cytometry coupled with live/dead staining identified an ultra-low biomass microbial ecosystem containing 3.62 x 10 4 cells/mL with a live fraction of 0.8%.Viable cells were also isolated and found to grow at subzero temperatures, high salinity (> 6%), and low pH (< pH 5), including one isolate List of
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".