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The Effect of Boron on Active Neutron Measurements: Application for the Mars Science Laboratory Dynamic Albedo of Neutrons Instrument

2020· article· en· W3195797437 on OpenAlexaboutno aff
Suzanne N. Nowicki, Sophie Festal, S. Czarnecki, C. Hardgrove, P. J. Gasda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
FundersLos Alamos National LaboratoryNational Nuclear Security AdministrationU.S. Department of Energy
KeywordsNeutron temperatureNeutronNeutron detectionNeutron moderatorNeutron fluxNeutron cross sectionNeutron sourceMars Exploration ProgramMartianMartian soilNeutron generatorBoronNeutron captureNeutron probeRadiochemistryNuclear physicsMaterials scienceMartian surfacePhysicsChemistryAstrobiology

Abstract

fetched live from OpenAlex

The primary objective of the Dynamic Albedo of Neutrons (DAN) experiment on board the Mars Science Laboratory (MSL) rover Curiosity is to assess the hydrogen content as the rover traverses the Martian surface. Because hydrogen is a light element, it is an efficient moderator for neutrons. The method used to estimate the hydrogen content by the DAN instrument is to measure the thermal neutron count rate emitted from the surface of the soil using a Pulsed Neutron Generator as an activation source coupled with a thermal neutron detector. However, boron has a high cross section for thermal neutron capture and can affect the thermal neutron flux measured by the DAN instrument. Recently, the MSL ChemCam instrument has shown high concentrations of B in the veins of the Murray formation and Yellowknife Bay at concentrations of 100 to 500 ppm. We show that the number of neutrons that are captured in the Martian soil increases with increasing B, resulting in reduced count rates observed by the DAN thermal neutron detector, which can lead to an overestimate of the hydrogen content.

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 score0.399
Threshold uncertainty score0.219

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)0.0000.000

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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