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Record W4200050910 · doi:10.1089/ast.2021.0111

The Scientific Importance of Returning Airfall Dust as a Part of Mars Sample Return (MSR)

2021· article· en· W4200050910 on OpenAlexaff
M. M. Grady, Roger E. Summons, T. D. Swindle, Francès Westall, Gerhard Kminek, Michael A. Meÿer, David Beaty, Brandi L. Carrier, T. Haltigin, L. E. Hays, Carl B. Agee, H. Busemann, Barbara Cavalazzi, Charles S. Cockell, Vinciane Debaille, Daniel P. Glavin, Ernst Hauber, Aurore Hützler, Bernard Marty, F. M. McCubbin, Lisa M. Pratt, A. B. Regberg, Alvin L. Smith, C. L. Smith, K. T. Tait, Nicholas J. Tosca, Arya Udry, Tomohiro Usui, M. A. Velbel, M. Wadhwa, María‐Paz Zorzano

Bibliographic record

VenueAstrobiology · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsRoyal Ontario MuseumCanadian Space Agency
Fundersnot available
KeywordsMars Exploration ProgramMartianAstrobiologyAtmospheric sciencesEnvironmental scienceEarth scienceAtmosphere of MarsEnvironmental chemistryGeologyChemistryPhysics

Abstract

fetched live from OpenAlex

Dust transported in the martian atmosphere is of intrinsic scientific interest and has relevance for the planning of human missions in the future. The MSR Campaign, as currently designed, presents an important opportunity to return serendipitous, airfall dust. The tubes containing samples collected by the Perseverance rover would be placed in cache depots on the martian surface perhaps as early as 2023–24 for recovery by a subsequent mission no earlier than 2028–29, and possibly as late as 2030–31. Thus, the sample tube surfaces could passively collect dust for multiple years. This dust is deemed to be exceptionally valuable as it would inform our knowledge and understanding of Mars' global mineralogy, surface processes, surface-atmosphere interactions, and atmospheric circulation. Preliminary calculations suggest that the total mass of such dust on a full set of tubes could be as much as 100 mg and, therefore, sufficient for many types of laboratory analyses. Two planning steps would optimize our ability to take advantage of this opportunity: (1) the dust-covered sample tubes should be loaded into the Orbiting Sample container (OS) with minimal cleaning and (2) the capability to recover this dust early in the workflow within an MSR Sample Receiving Facility (SRF) would need to be established. A further opportunity to advance dust/atmospheric science using MSR, depending upon the design of the MSR Campaign elements, may lie with direct sampling and the return of airborne dust. Summary of Findings FINDING D-1: An accumulation of airfall dust would be an unavoidable consequence of leaving M2020 sample tubes cached and exposed on the surface of Mars. Detailed laboratory analyses of this material would yield new knowledge concerning surface-atmosphere interactions that operate on a global scale, as well as provide input to planning for the future robotic and human exploration of Mars. FINDING D-2: The detailed information that is possible from analysis of airfall dust can only be obtained by investigation in Earth laboratories, and thus this is an important corollary aspect of MSR. The same information cannot be obtained from orbit, from in situ analyses, or from analyses of samples drilled from single locations. FINDING D-3: Given that at least some martian dust would be on the exterior surfaces of any sample tubes returned to Earth, the capability to receive and curate dust in an MSR Sample Receiving Facility (SRF) is essential. SUMMARY STATEMENT: The fact that any sample tubes cached on the martian surface would accumulate some quantity of martian airfall dust presents a low-cost scientifically valuable opportunity. Some of this dust would inadvertently be knocked off as part of tube manipulation operations, but any dust possible should be loaded into the OS along with the sample tubes. This dust should be captured in an SRF and made available for detailed scientific analysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.226

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 science0.0000.000
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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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