MétaCan
Menu
Back to cohort
Record W3161315235 · doi:10.1061/9780784483374.005

Granular Morphology and Mineralogical Composition for Modeling Lunar Dust Behavior

2021· article· en· W3161315235 on OpenAlexaff
S. R. Deitrick, Jeffrey W. Bullard, Niven Shumaker, P. Suermann

Bibliographic record

VenueEarth and Space 2021 · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsInfrastructure Canada
Fundersnot available
KeywordsRegolithAstrobiologyMars Exploration ProgramMaterials scienceEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

The need for understanding lunar regolith characteristics is rapidly increasing as NASA prepares to create a long-term human presence on the Moon by the late 2020s. The characteristics of regolith affect not only human and rover mobility on the surface of the Moon, but also in situ resource utilization (ISRU) efforts such as resource processing, resource excavation, and in situ construction/additive manufacturing. Understanding the regolith is vital for lunar exploration, and emerging research is improving the body of knowledge that has long been poorly understood. Therefore, this research will seek to better understand the physical properties of lunar regolith. This will be done by analyzing multiple Apollo lunar regolith and lunar regolith simulant samples to acquire highly accurate morphological, chemical, and mineralogical properties of each sample. These data, and in turn the knowledge it will convey, will help inform applied methods for advancing NASA objectives in their mission to the Moon and on to Mars. X-ray microtomography (XCT) will be used to obtain 3D shapes and sizes of thousands of lunar regolith and simulant particles. These data, along with mineralogical and compositional information through scanning electron microscopy (SEM) and X-ray diffraction (XRD), will be used to create virtual lunar regolith samples for free use by the scientific community. The final product will be a curated, expandable data repository of physical properties of both lunar regolith and regolith simulants. The data repository will provide the basis for high-accuracy calculations of optical properties, optical scattering characteristics, dielectric polarizability, maximum packing fractions, and other physical and geotechnical properties that depend on the shape and size distribution of the regolith. It will also provide a means of determining how well a given regolith simulant imitates the properties of the lunar regolith to which it corresponds. This work is vital to ensuring that Earth-based regolith simulants are accurate enough to test for lunar applications and prepare for data-informed methods for applied lunar engineering, mining, and construction.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.210

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEarth and Space 2021Same topicPlanetary Science and ExplorationFrench-language works237,207