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Record W2899518371

The Mars 2020 Candidate Landing Sites: A Magnetic Field Perspective

2018· book-chapter· en· W2899518371 on OpenAlexaff
Anna Mittelholz, A. Morschhauser, C. L. Johnson, B. Langlais, R. J. Lillis, Foteini Vervelidou, B. P. Weiss

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2018
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMars Exploration ProgramPerspective (graphical)Field (mathematics)AstrobiologyGeologyComputer scienceArtificial intelligencePhysicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

We present an analysis of the remaining three candidate landing sites for Mars 2020, Columbia Hills (CH), Northeast Syrtis (NES) and Jezero (JE) from the perspective of understanding Mars' crustal magnetic field. We identify how the different sites can address each of six community‐defined paleomagnetic science objectives for Mars return samples. These objectives include understanding the early dynamo field and its variability, identification of magnetic minerals that carry magnetization in the samples, and characterization of any thermal and chemical alteration of samples. Satellite data have provided global and regional constraints on crustal magnetization, indicating strong magnetizations at CH, and weak to no magnetization at JE and NES. However, the primary paleomagnetic interest – understanding the early dynamo – requires ground truth from a landing site at which pre‐Noachian and early Noachian deposits are accessible. This requirement is most likely met by the site NES which contains meggabreccia deposits, and it is therefore the highest priority landing site for magnetic field investigations. Importantly, a sample return mission has never been done and so any of the three landing sites will provide critical, new data that will contribute to understanding the history of Mars' magnetic field and crustal mineralogy and, in turn, their constraints on the planet's evolution.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.228
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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