Survivability of Biomolecules Under Energetic Processes for the Identification of Biosignatures on Mars
Bibliographic record
Abstract
Missions on Mars have already demonstrated its past habitability (Arvidson et al., 2014; Fornaro et al., 2018a; Grotzinger et al., 2014) and the search for biosignatures, such as the identification of organic molecules, has become one of the main goals of exploration programs (Fornaro et al., 2020; Horneck et al., 2016). Specifically, data collected by the SAM instrument showed the presence of thiophenes, aromatics, aliphatic and thiol derivatives in the Murray and Sheepbed mudstones, similar to the analysis of the Tissint Mars meteorite (Eigenbrode et al., 2018). However, organics on the surface of Mars are continuously exposed to harsh environmental conditions. Among them, the UV and ion radiation is known for its critical implications on the organic matter present in the soil.Thus, an understanding of the environment in which organic matter evolves on the Martian surface is important. Specifically, minerals play a crucial role in the processes experienced by organic molecules on Mars, influencing their chemical evolution. The preservation state of organic molecules is often regulated by their interaction with the mineral phase in which they are embedded. Investigations on the catalytic and protective properties of different Martian minerals under Mars-like conditions have been carried out (Fornaro et al. 2018b), who concluded that in several paleoenvironments on Earth, the long-term preservation of terrestrial biosignatures is attributed to sedimentary materials, in particular phosphates, silica, clays, carbonates and metalliferous materials. However, the simple classification of Martian minerals as catalytic or protective is not possible because the behaviour of minerals under Martian conditions depends on the organic molecules involved and their specific interactions with the mineral surface sites. It is therefore important to study the response of specific molecule-mineral complexes to UV and ion irradiation.In this work, we investigated the likelihood that amino acids and fatty acids biomarker embedded in minerals would be preserved despite Martian chemical weathering by energetic irradiation, and therefore be observable by analytical techniques on board of Rosalind Franklin rover.ReferencesArvidson RE, Squyres SW, Bell JF, et al. Ancient Aqueous Environments at Endeavour Crater, Mars. Science 2014;343(6169):1248097; doi: 10.1126/science.1248097. Eigenbrode JL, Summons RE, Steele A, et al. Organic Matter Preserved in 3-Billion-Year-Old Mudstones at Gale Crater, Mars. Science 2018;360(6393):1096–1101; doi: 10.1126/science.aas9185. Fornaro T, Steele A and Brucato JR. Catalytic/Protective Properties of Martian Minerals and Implications for Possible Origin of Life on Mars. Life 2018a; 8(4):56; doi: 10.3390/life8040056. Fornaro T, Boosman A, Brucato JR, et al. UV Irradiation of Biomarkers Adsorbed on Minerals under Martian-like Conditions: Hints for Life Detection on Mars. Icarus 2018b; 313:38–60; doi: 10.1016/j.icarus.2018b.05.001. Fornaro T, Brucato J, Poggiali G, et al. UV Irradiation and Near Infrared Characterization of Laboratory Mars Soil Analog Samples: The Case of Phthalic Acid, Adenosine 5-Monophosphate, L-Glutamic Acid and L-Phenylalanine Adsorbed onto the Clay Mineral Montmorillonite in the Presence of Magnesium Perchlorate. 2020; doi: 10.20944/preprints202003.0172.v1. Grotzinger JP, Sumner DY, Kah LC, et al. A Habitable Fluvio-Lacustrine Environment at Yellowknife Bay,Gale Crater, Mars. Science 2014;343(6169):1242777; doi: 10.1126/science.1242777. Horneck G, Walter N, Westall F, et al. AstRoMap European Astrobiology Roadmap. Astrobiology 2016;16(3):201.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".