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Record W3092320717 · doi:10.1038/s41598-020-73846-y

ExoMars Raman Laser Spectrometer (RLS): development of chemometric tools to classify ultramafic igneous rocks on Mars

2020· article· en· W3092320717 on OpenAlexaff
Marco Veneranda, G. López-Reyes, J. A. Manrique, Aurelio Sanz‐Arranz, E.A. Lalla, Menelaos Konstantinidis, Andoni Moral, J. Medina, F. Rull

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersHorizon 2020 Framework ProgrammeMinisterio de Economía y Competitividad
KeywordsUltramafic rockMars Exploration ProgramRaman spectroscopyRaman laserMartianPyroxeneGeologyIgneous rockAstrobiologyOlivineMineralSpectrometerRemote sensingMineralogyGeochemistryMaterials scienceOpticsRaman scatteringPhysics

Abstract

fetched live from OpenAlex

This work aims to evaluate whether the multi-point analysis the ExoMars Raman Laser Spectrometer (RLS) will perform on powdered samples could serve to classify ultramafic rocks on Mars. To do so, the RLS ExoMars Simulator was used to study terrestrial analogues of Martian peridotites and pyroxenites by applying the operational constraints of the Raman spectrometer onboard the Rosalind Franklin rover. Besides qualitative analysis, RLS-dedicated calibration curves have been built to estimate the relative content of olivine and pyroxenes in the samples. These semi-quantitative results, combined with a rough estimate of the concentration ratio between clino- and ortho-pyroxene mineral phases, were used to classify the terrestrial analogues. XRD data were finally employed as reference to validate Raman results. As this preliminary work suggests, ultramafic rocks on Mars could be effectively classified through the chemometric analysis of RLS data sets. After optimization, the proposed chemometric tools could be applied to the study of the volcanic geological areas detected at the ExoMars landing site (Oxia Planum), whose mineralogical composition and geological evolution have not been fully understood.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.033
GPT teacher head0.232
Teacher spread0.199 · 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 designBench or experimental
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

Citations34
Published2020
Admission routes1
Has abstractyes

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