Sample Collection and Return from Mars: Optimising Sample Collection Based on the Microbial Ecology of Terrestrial Volcanic Environments
Bibliographic record
Abstract
Abstract With no large-scale granitic continental crust, all environments on Mars are fundamentally derived from basaltic sources or, in the case of environments such as ices, evaporitic, and sedimentary deposits, influenced by the composition of the volcanic crust. Therefore, the selection of samples on Mars by robots and humans for investigating habitability or testing for the presence of life should be guided by our understanding of the microbial ecology of volcanic terrains on the Earth. In this paper, we discuss the microbial ecology of volcanic rocks and hydrothermal systems on the Earth. We draw on microbiological investigations of volcanic environments accomplished both by microbiology-focused studies and Mars analog studies such as the NASA BASALT project. A synthesis of these data emphasises a number of common patterns that include: (1) the heterogeneous distribution of biomass and diversity in all studied materials, (2) physical, chemical, and biological factors that can cause heterogeneous microbial biomass and diversity from sub-millimetre scales to kilometre scales, (3) the difficulty ofa prioriprediction of which organisms will colonise given materials, and (4) the potential for samples that are habitable, but contain no evidence of a biota. From these observations, we suggest an idealised strategy for sample collection. It includes: (1) collection of multiple samples in any given material type (∼9 or more samples), (2) collection of a coherent sample of sufficient size ( ${\sim}10~\mbox{cm}^{3}$ ∼10cm3 ) that takes into account observed heterogeneities in microbial distribution in these materials on Earth, and (3) collection of multiple sample suites in the same material across large spatial scales. We suggest that a microbial ecology-driven strategy for investigating the habitability and presence of life on Mars is likely to yield the most promising sample set of the greatest use to the largest number of astrobiologists and planetary scientists.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".