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Record W2584626650

Automated Grouping of Opportunity Rover Alpha Particle X-ray Spectrometer Compositional Data

2016· article· en· W2584626650 on OpenAlexaff
S. J. VanBommel, R. Gellert, B. C. Clark, D. W. Ming, D. W. Mittlefehldt, Christian Schröder, A. S. Yen

Bibliographic record

VenueAGU Fall Meeting Abstracts · 2016
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsContext (archaeology)WeightingSet (abstract data type)Consistency (knowledge bases)MineralogyGeologyData miningMathematicsAlgorithmComputer sciencePhysicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

The Alpha Particle X-ray Spectrometer (APXS) conducts high-precision in situ measurements of rocks and soils on both active NASA Mars rovers. Since 2004 the rover Opportunity has acquired around 440 unique APXS measurements, including a wide variety of compositions, during its 42+ kilometers traverse across several geological formations. Here we discuss an analytical comparison algorithm providing a means to cluster samples due to compositional similarity and the resulting automated classification scheme. Due to the inherent variance of elements in the APXS data set, each element has an associated weight that is inversely proportional to the variance. Thus, the more consistent the abundance of an element in the data set, the more it contributes to the classification. All 16 elements standard to the APXS data set are considered. Careful attention is also given to the errors associated with the composition measured by the APXS - larger uncertainties reduce the weighting of the element accordingly. The comparison of two targets, i and j, generates a similarity score, S(sub ij). This score is immediately comparable to an average ratio across all elements if one assumes standard weighted uncertainty. The algorithm facilitates the classification of APXS targets by chemistry alone - independent of target appearance and geological context which can be added later as a consistency check. For the N targets considered, a N by N hollow matrix, S, is generated where S = S(sup T). The average relation score, S(sub av), for target N(sub i) is simply the average of column i of S. A large S(sub av) is indicative of a unique sample. In such an instance any targets with a low comparison score can be classified alike. The threshold between classes requires careful consideration. Applying the algorithm to recent Marathon Valley targets indicates similarities with Burns formation and average-Mars-like rocks encountered earlier at Endeavour Crater as well as a new class of felsic rocks.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.047
GPT teacher head0.264
Teacher spread0.217 · 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 designSimulation or modeling
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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