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Results From the Insight Mission After a Year and a Half on Mars

2020· article· en· W3092969856 on OpenAlexaff
W. T. Pike, W. B. Banerdt, S. E. Smrekar, Philippe Lognonné, Domenico Giardini, D. Banfield, V. Dehant, W. M. Folkner, M. P. Golombek, C. L. Johnson, C. T. Russell, Aymeric Spiga, Tilman Spohn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMars Exploration ProgramObservatorySeismometerGeophysicsPhysicsGeodesyGeologyAstrobiologyAstrophysicsSeismology

Abstract

fetched live from OpenAlex

The InSight mission landed on Mars in November of 2018 and completed installation of a seismometer (SEIS) on the surface about two months later. In addition to SEIS, InSight carries a diverse geophysical observatory including a heat flow and sub-surface physical properties experiment (HP3), a geodesy (planetary rotation dynamics) experiment (RISE), and a suite of environmental sensors measuring the magnetic field and atmospheric temperature, pressure and wind (APSS). For more than a year, SEIS has been providing near-continuous seismic monitoring of Mars, with background noise levels orders of magnitude lower than that achievable on the Earth. Since installation was completed, the SEIS team has identified more than 400 events that we have not attributed to the local environment or spacecraft activity, and dozens that appear to be marsquakes of tectonic origin. We present an overview of observations by the SEIS instrument as well as a summary of other geophysical observations made by InSight during the past year and a half.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.021
GPT teacher head0.202
Teacher spread0.181 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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