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Record W2921971297 · doi:10.1089/ast.2018.1869

The BASALT Research Program: Designing and Developing Mission Elements in Support of Human Scientific Exploration of Mars

2019· article· en· W2921971297 on OpenAlexaff
D. S. S. Lim, Andrew F. J. Abercromby, S. E. Kobs Nawotniak, David Lees, Michael J. Miller, Allyson L. Brady, Matthew J. Miller, Zara Mirmalek, A. Sehlke, Samuel J. Payler, Adam Stevens, C. W. Haberle, Kara H. Beaton, Steven P. Chappell, S. S. Hughes, Charles S. Cockell, R. C. Elphic, M. Downs, J. L. Heldmann

Bibliographic record

VenueAstrobiology · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMcMaster University
FundersPlanetary Science DivisionJohnson Space CenterSolar System Exploration Research Virtual InstituteU.S. Geological SurveyNational Aeronautics and Space Administration
KeywordsMars Exploration ProgramHabitabilityBasaltExploration of MarsTerrainEarth scienceAstrobiologyGeologySystems engineeringComputer scienceEngineeringPlanetGeographyGeochemistry

Abstract

fetched live from OpenAlex

The articles associated with this Special Collection focus on the NASA BASALT (Biologic Analog Science Associated with Lava Terrains) Research Program, which aims at answering the question, "How do we support and enable scientific exploration during human Mars missions?" To answer this the BASALT team conducted scientific field studies under simulated Mars mission conditions to both broaden our understanding of the habitability potential of basalt-rich terrains on Mars and examine the effects of science on current Mars mission concepts of operations. This article provides an overview of the BASALT research project, from the science, to the operational concepts that were tested and developed, to the technical capabilities that supported all elements of the team's research. Further, this article introduces the 12 articles that are included in this Special Collection.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.105
GPT teacher head0.372
Teacher spread0.267 · 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 designTheoretical or conceptual
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

Citations61
Published2019
Admission routes1
Has abstractyes

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