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Record W4303427375 · doi:10.3389/fspas.2022.1016359

LunaR: Overview of a versatile Raman spectrometer for lunar exploration

2022· article· en· W4303427375 on OpenAlexafffund
E. A. Cloutis, C. M. Caudill, E.A. Lalla, J. Newman, M. G. Daly, Elizabeth A. Lymer, J. Freemantle, Roman V. Kruzelecky, D. M. Applin, H. Chen, Stephanie Connell, Diana M. Fernandes, F. Giusto, J. Hawke, J. Lamamry, P. Murzionak, Alexis Parkinson, Qihan Peng, Nathalie Turenne, Z. U. Wolf

Bibliographic record

VenueFrontiers in Astronomy and Space Sciences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMPB Technologies & Communications (Canada)York UniversityUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyUniversity of WinnipegResearch Manitoba
KeywordsSpectrometerBreadboardRaman spectroscopyNadirRemote sensingContext (archaeology)Mars Exploration ProgramOpticsAstrobiologyPhysicsGeologyAstronomySatellite

Abstract

fetched live from OpenAlex

The LunaR concept study investigated the scientific value, feasibility, and deployment options for a Raman spectrometer on future lunar landed missions. It consists of a breadboard instrument that covers the 150–4000 cm −1 wavelength range with a resolution of ∼6 cm −1 ; Raman scattering is induced by a 532 nm continuous wave laser. The current conceptual design envisions the Raman spectrometer performing a downward-looking, 90-point one-dimensional across-track scan (±45°off nadir) of the lunar surface with the instrument mounted on the underside of a rover. A downward-looking context camera would provide information on the physical nature of targets interrogated by the Raman spectrometer and localization of the Raman spectra. Our laboratory investigations indicate that Raman spectroscopy is applicable to addressing a wide range of lunar surface exploration goals related to geology, in situ resource identification, and condensed volatile detection in diverse geological terrains, including permanently shadowed regions. Testing of a breadboard and commercial instrument on lunar samples and analogues indicates that a complete spectral scan of a target of interest can be completed in ∼90 min, permitting its use on even short-duration lunar landed missions. All of the major minerals present on the Moon can be detected, and in many cases their compositions can be quantified or constrained.

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 score0.720
Threshold uncertainty score0.343

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 communication0.0000.001
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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