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Record W3035003079 · doi:10.1038/s41598-020-68715-7

Investigating the biological potential of galactic cosmic ray-induced radiation-driven chemical disequilibrium in the Martian subsurface environment

2020· article· en· W3035003079 on OpenAlexfundno aff

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsDisequilibriumMartianCosmic rayAtmosphere of MarsAtmosphere (unit)Extant taxonMars Exploration ProgramMartian surfaceRadiolysisSurface water

Abstract

fetched live from OpenAlex

There is growing evidence suggesting the presence of aqueous environment on ancient Mars, raising the question of the possibility of life in such an environment. Subsequently, with the erosion of the Martian atmosphere resulting in drastic changes in its climate, surface water disappeared, shrinking habitable spaces on the planet, with only a limited amount of water remaining near the surface in form of brines and water-ice deposits. Life, if it ever existed, would have had to adapt to harsh modern conditions, which includes low temperatures and surface pressure, and high radiation dose. Presently, there is no evidence of any biological activity on the planet's surface, however, the subsurface environment, which is yet to be explored, is less harsh, has traces of water in form of water-ice and brines, and undergoes radiation-driven redox chemistry. I hypothesize that Galactic Cosmic Ray (GCR)-induced radiation-driven chemical disequilibrium can be used for metabolic energy by extant life, and host organisms using mechanisms seen in similar chemical and radiation environments on Earth. I propose a GCR-induced radiolytic zone, and discuss the prospects of finding such life with Rosalind Franklin rover of the ExoMars mission.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.218
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

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