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Record W4200182161 · doi:10.1089/ast.2021.0198

Mars Sample Return (MSR): Planning for Returned Sample Science

2021· article· en· W4200182161 on OpenAlexaff
Gerhard Kminek, Michael A. Meÿer, D. W. Beaty, Brandi L. Carrier, T. Haltigin, L. E. Hays

Bibliographic record

VenueAstrobiology · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsMars Exploration ProgramAstrobiologyExploration of MarsMartianSample (material)Mars landingMartian surfaceSpace researchNASA Chief ScientistSpace explorationEnvironmental scienceComputer scienceEarth scienceRemote sensingAeronauticsGeologyAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Mars Sample Return (MSR) has been a high priority for the planetary science community for more than four decades. Analyzing martian samples in terrestrial laboratories would advance our understanding of Mars in multiple ways that are impossible using in situ missions alone. The overall MSR concept includes three distinct phases: 1. Selecting and collecting scientifically suitable samples on Mars, currently being carried out by the Mars 2020 mission with the Perseverance rover; 2. Retrieving the samples on Mars and transporting them to Earth; 3. Receiving the samples on Earth, making them available for analysis by the science community for decades to come. With the recent successful collection of the first samples by the Perseverance rover and the ongoing progress by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) on the development of the missions that could retrieve and transport the samples to Earth, MSR continues to move closer to becoming a reality. As Perseverance and the MSR retrieval and return missions progress, it becomes increasingly imperative to develop detailed plans for the receipt and analysis of the samples on Earth to ensure that the full science potential of MSR can be realized.

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 score0.499
Threshold uncertainty score0.362

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 head0.028
GPT teacher head0.278
Teacher spread0.250 · 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

Citations40
Published2021
Admission routes1
Has abstractyes

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