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Record W767355545

What Can the Curiosity Rover Tell Us About the Climate of Mars

2013· article· en· W767355545 on OpenAlexaboutno aff
R. M. Haberle

Bibliographic record

VenueNASA STI Repository (National Aeronautics and Space Administration) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsnot available
FundersUniversity of WashingtonAmes Research CenterNational Aeronautics and Space Administration
KeywordsMars Exploration ProgramCuriosityAstrobiologyHabitabilityGeologyAtmosphere (unit)PlanetEarth scienceMeteorologyGeography
DOInot available

Abstract

fetched live from OpenAlex

What Can the Curiosity Rover Tell Us About the Climate of Mars? Assessing the habitability of Gale Crater is the goal of the Curiosity Rover, which has been gathering data since landing on the Red Planet last August. To meet that goal, Curiosity brought with it a suite of instruments to measure the biological potential of the landing site, the geology and chemistry of its surface, and local environmental conditions. Some of these instruments illuminate the nature of the planet fs atmosphere and climate system, both for present day conditions as well as for conditions that existed billions of years ago. For present day conditions, Curiosity has a standard meteorology package that measures pressure, temperature, winds and humidity, plus a sensor the measures the UV flux. These data confirm what we learned from previous missions namely that today Mars is a cold, dry, and barren desert-like planet. For past conditions, however, wetter and probably warmer conditions are indicated. Curiosities cameras reveal gravel beds that must have formed by flowing rivers, and sedimentary deposits of layered sand and mudstones possibly associated with lakes. An ancient aqueous environment is further supported by the presence of sulfate veins coursing through some of the rocks in Yellowknife Bay where Curiosity is planning its first drilling activity. I will discuss these results and their implications in this lecture.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.508

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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