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Record W4387702593 · doi:10.1038/s43247-023-01042-7

Microbial growth in actual martian regolith in the form of Mars meteorite EETA79001

2023· article· en· W4387702593 on OpenAlexfundno aff
Neveda Naz, Bijan F. Harandi, J. S. Newmark, Samuel P. Kounaves

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersJohnson Space CenterNuclear Safety and Security CommissionLaidlaw FoundationNational Aeronautics and Space Administration
KeywordsRegolithMartianAstrobiologyMars Exploration ProgramMartian soilMeteoriteLife on MarsMartian surfaceEnvironmental scienceGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Studies to understand the growth of organisms on Mars are hampered by the use of simulants to duplicate martian mineralogy and chemistry. Even though such materials are improving, no terrestrial simulant can replace a real martian sample. Here we report the use of actual martian regolith, in the form of Mars meteorite EETA79001 sawdust, to demonstrate its ability to support the growth of four microorganisms, E. coli . Eucapsis sp., Chr20-20201027-1 , and P. halocryophilus , for up to 23 days under terrestrial conditions using regolith:water ratios from 4:1 to 1:10. If the EETA79001 sawdust is widely representative of regolith on the martian surface, our results imply that microbial life under appropriate conditions could have been present on Mars in the past and/or today in the subsurface, and that the regolith does not contain any bactericidal agents. The results of our study have implications not only for putative martian microbial life but also for building bio-sustainable human habitats on Mars.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.228
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2023
Admission routes1
Has abstractyes

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