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Record W3190699594 · doi:10.1002/jrs.6215

Analytical database of Martian minerals (ADaMM): Project synopsis and Raman data overview

2021· article· en· W3190699594 on OpenAlexaff
Marco Veneranda, Aurelio Sanz‐Arranz, J. A. Manrique, Jesús Sáiz, Clara Garcia‐Prieto, Elena Pascual‐Sánchez, J. Medina, Menelaos Konstantinidis, E.A. Lalla, Andoni Moral, L. M. Nieto, F. Rull, G. López-Reyes

Bibliographic record

VenueJournal of Raman Spectroscopy · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersH2020 European Research CouncilHorizon 2020 Framework ProgrammeMinisterio de Economía y Competitividad
KeywordsMars Exploration ProgramMartianExploration of MarsRaman spectroscopySpectrometerMartian surfaceComputer scienceRemote sensingSpace explorationData collectionDatabaseAstrobiologySystems engineeringVisualizationEnvironmental scienceGeologyAerospace engineeringEngineeringPhysicsData miningOptics

Abstract

fetched live from OpenAlex

Abstract The Mars2020/Perseverance and ExoMars/Rosalind Franklin rovers are both slated to return the first Raman spectra ever collected from another planetary surface, Mars. In order to optimize the rovers scientific outcome, the scientific community needs to be provided with tailored tools for data treatment and interpretation. Responding to this need, the purpose of the Analytical Database of Martian Minerals (ADaMM) project is to build an extended multianalytical database of mineral phases that have been detected on Mars or are expected to be found at the landing sites where the two rovers will operate. Besides the use of conventional spectrometers, the main objective of the ADaMM database is to provide access to data collected by means of laboratory prototypes simulating the analytical performances of the spectroscopic systems onboard the Mars 2020 and ExoMars rovers. Planned to be released to the public in 2022, ADaMM will also provide access to data treatment and visualization tools developed in the framework of the mentioned space exploration missions. As such, the present work seeks to provide an overview of the ADaMM online platform, spectral tools, and mineral collection. In addition to that, the manuscript describes the Raman spectrometers used to analyze the mineral collection and presents a representative example of the analytical performance ensured by the Raman prototypes assembled to simulate the Raman Laser Spectrometer (RLS) and SuperCam systems.

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 score0.212
Threshold uncertainty score0.379

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 head0.060
GPT teacher head0.335
Teacher spread0.275 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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