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Record W4386258129 · doi:10.3847/psj/ace9c1

Exploring Martian Magnetic Fields with a Helicopter

2023· article· en· W4386258129 on OpenAlex
Anna Mittelholz, Lindsey J. Heagy, C. L. Johnson, J. Bapst, J. R. Espley, A. A. Fraeman, B. Langlais, R. J. Lillis, W. Rapin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Planetary Science Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCentre National d’Etudes SpatialesCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsMars Exploration ProgramMartianMagnetometerExploration of MarsRemote sensingSystems engineeringWork (physics)Plan (archaeology)Range (aeronautics)AstrobiologyAerospace engineeringComputer scienceGeologyEngineeringPhysicsMagnetic fieldMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The era of helicopter-based surveys on Mars has already begun, creating opportunities for future aerial science investigations with a range of instruments. We argue that magnetometer-based studies can make use of aerial technology to answer some of the key questions regarding early Mars evolution. As such, we discuss mission concepts for a helicopter equipped with a magnetometer on Mars, measurements it would provide, and survey designs that could be implemented. For a range of scenarios, we build magnetization models and test how well structures can be resolved using a range of different inversion approaches. With this work, we provide modeling ground work and recommendations to plan the future of aerial Mars exploration.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.222
Teacher spread0.167 · 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