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Record W2801925191 · doi:10.1139/tcsme-2007-0041

DESIGN OF A WIND POWERED MARS ROVER

2007· article· en· W2801925191 on OpenAlexaffvenueabout
Zavie Berenbaum, Elza Brunelle-Yeung, Patrice Castonguay, Boby Chu, Luke Chapman, Michael Hayatian, Jasmin Manseau, Michael Depaoli, Leo Hartman, Alexei A. Morozov

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsCanadian Space AgencyMcGill University
Fundersnot available
KeywordsMars Exploration ProgramMars roverRange (aeronautics)Aerospace engineeringComputer sciencePerspective (graphical)Exploration of MarsSystems engineeringAeronauticsEnvironmental scienceAstrobiologyEngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

There has been considerable interest in the unmanned exploration of Mars for quite some time [4][7][8] but the current generation of rovers can explore only a small portion of the total planetary surface. One approach to addressing this deficiency is to consider a rover that has greater range and that is cheaper so that it can be deployed in greater numbers. The option explored in this paper uses the wind to propel a rover platform, trading off precise navigation for greater range. The capabilities of such a rover lie between the global perspective of orbiting satellites and the detailed local analysis of current-generation rovers. The design of the Wind-Powered Mars Rover is discussed, and the prototype built at McGill University as part of a student design course is described.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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 designSimulation or modeling
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
Published2007
Admission routes3
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAerospace Engineering and Energy SystemsFrench-language works237,207