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Record W3048172467

Preparations for ExoMars: Learning Lessons from Curiosity

2016· article· en· W3048172467 on OpenAlexaboutno aff
P. Edwards, I. H. Hutchinson, Sally Morgan, Melissa McHugh, Cédric Malherbe, Hannah Lerman, Richard Ingley

Bibliographic record

VenueORBi (University of Liège) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
Fundersnot available
KeywordsCuriosityAstrobiologyGeologyComputer sciencePsychologyBiologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

In 2020, the European Space Agency will launch its first Mars rover mission, ExoMars. The rover will use a drill to obtain samples from up to 2m below the Martian surface that will then be analysed using a variety of analytical instruments, including the Raman Laser Spectrometer (RLS), which will be the first Raman spectrometer to be used on a planetary mission.To prepare for ExoMars RLS operations, we report on a series of experiments that have been performed in order to investigate the response of a representative Raman instrument to a number of analogue samples (selected based on the types of material known to be important, following investigations performed by NASA's Mars Science Laboratory, MSL, on the Curiosity rover). Raman spectroscopy will provide molecular and mineralogical information about the samples obtained from the drill cores on ExoMars. MSL acquires similar information using the CheMin XRD instrument which analyses samples acquired from drill holes several centimetres deep. Like Raman spectroscopy, XRD also provides information on the mineralogical makeup of the analysed samples.The samples in our study were selected based on CheMin data obtained from drill sites at Yellowknife Bay, one of the first locations visited by Curiosity (supplemented with additional fine scale elemental information obtained with the ChemCam LIBS laser instrument). Once selected (or produced), the samples were characterised using standard laboratory XRD and XRF instruments (in order to compare with the data obtained by CheMin) and a standard, laboratory based LIBS system (in order to compare with the ChemCam data). This characterisation provides confirmation that the analogue samples are representative of the materials likely to be encountered on Mars by the ExoMars rover.A representative, miniaturised Raman spectrometer was used to analyse the samples, using acquisition strategies and operating modes similar to those expected for the ExoMars instrument. The type of minerals detected are identified and compared to the information typically acquired using other analytical science techniques investigating in order to highlight the benefits and drawbacks of using Raman spectroscopy for planetary science applications.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.725

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)0.0010.000

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 head0.247
Teacher spread0.225 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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