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Record W4321490883 · doi:10.5194/egusphere-egu23-2924

Constraining the Chemistry of Sub-cm Diagenetic Features with Curiosity's Alpha Particle X-ray Spectrometer

2023· preprint· en· W4321490883 on OpenAlexaff
S. J. VanBommel, Jeffrey S. Berger, R. Gellert, C. D. O’Connell‐Cooper, L. M. Thompson, Michael McCraig, A. S. Yen, John R. Christian, Abigail L. Knight, N. I. Boyd

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of New BrunswickUniversity of Guelph
Fundersnot available
KeywordsDiagenesisSpectrometerMineralogyGeologyPhysicsRemote sensingOptics

Abstract

fetched live from OpenAlex

The Alpha Particle X-ray Spectrometer (APXS) onboard the Mars Science Laboratory (MSL) rover Curiosity has acquired approximately 1,300 geochemical analyses since landing in 2012. The APXS utilizes a combination of X-ray fluorescence and particle-induced X-ray emission to determine the chemical composition of materials within its 15+ mm diameter field of view (FOV) [1, 2]. Diagenetic features provide a means to further understand and constrain the habitability of Curiosity's landing site, Gale crater. These features often present as veins or nodules with an areal extent on the sub-cm scale. APXS analyses of these features therefore contain a mixture of signals from the feature and host substrate.To probe the composition of these sub-FOV features, Curiosity has developed a technique whereby multiple APXS measurements are conducted in close proximity to the primary target (referred to as a raster). The data are then analyzed to not only localize APXS FOVs, mitigating arm placement uncertainty which is on the order of 1-2 cm [2], but also infer the composition of the various endmembers within the workspace. The original raster analysis method (e.g., [2, 3]) has proven useful at deconvolving the chemistry of diagenetic features from the surrounding substrate. However, this method utilizes APXS oxide data as the primary input. These data are derived assuming a homogeneous sample for the purposes of calculating and correcting for matrix effects (the attenuation of induced X-rays by other elements in the sample). In instances of clear chemical heterogeneities, these matrix corrections can result in skewed compositions of the derived endmembers, such as a vein or nodule.Here we present an improvement to this method whereby we utilize low-level data products and isolate matrix effect calculations for each individual endmember (e.g., [4]). The derived results show significant improvements (10-30%) compared to the oxide method in stoichiometric ratios when applied to calcium sulfate veins, an ideal proof-of-concept sample. Subsequent analyses of magnesium-sulfate dominated nodules hint at other potential mobile elements within the fluids present during diagenesis, such as P, Mn, Ni, and/or Zn. Similar elements were enriched in nodules at the Ayton/Groken field site, where P2O5 and MnO concentrations in the nodular material totaled over 25 wt% at a ~2:1 P:Mn molar ratio [4]. The improved analytical method will be particularly useful as Curiosity continues to explore the Marker Band and sulfate unit.[1] Gellert & Clark (2015), Elements, 11.[2] VanBommel et al. (2016), XRS, 45.[3] VanBommel et al. (2017), XRS, 46.[4] VanBommel et al. (2023), Icarus, 392.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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