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Record W1483806214 · doi:10.1002/2014je004622

Overview of the Mars Science Laboratory mission: Bradbury Landing to Yellowknife Bay and beyond

2014· article· en· W1483806214 on OpenAlexaffabout
A. R. Vasavada, J. P. Grotzinger, R. E. Arvidson, F. J. Calef, J. A. Crisp, Sanjeev Gupta, J. A. Hurowitz, N. Mangold, S. Maurice, M. E. Schmidt, R. C. Wiens, R. M. E. Williams, R. A. Yingst

Bibliographic record

VenueJournal of Geophysical Research Planets · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsBrock University
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsCobbleMars Exploration ProgramGeologyPebbleHabitabilityAstrobiologyEarth scienceBasaltArchaeologyOceanographyGeochemistryGeomorphologyPlanetGeography

Abstract

fetched live from OpenAlex

Abstract The Mars Science Laboratory mission reached Bradbury Landing in August 2012. In its first 500 sols, the rover Curiosity was commissioned and began its investigation of the habitability of past and present environments within Gale Crater. Curiosity traversed eastward toward Glenelg, investigating a boulder with a highly alkaline basaltic composition, encountering numerous exposures of outcropping pebble conglomerate, and sampling aeolian sediment at Rocknest and lacustrine mudstones at Yellowknife Bay. On sol 324, the mission turned its focus southwest, beginning a year‐long journey to the lower reaches of Mt. Sharp, with brief stops at the Darwin and Cooperstown waypoints. The unprecedented complexity of the rover and payload systems posed challenges to science operations, as did a number of anomalies. Operational processes were revised to include additional opportunities for advance planning by the science and engineering teams.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.358
Threshold uncertainty score0.712

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.007

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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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

Citations136
Published2014
Admission routes2
Has abstractyes

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