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Record W1664688733

Robotic Technologies for Space Exploration at MDA

2005· article· en· W1664688733 on OpenAlexaboutno aff
C. Sallaberger, P. Fulford, C Ower, Naureen Ghafoor, R. McCoubrey

Bibliographic record

VenueInternational Symposium on Artificial Intelligence · 2005
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsnot available
Fundersnot available
KeywordsRoboticsInternational Space StationRendezvousExploration of MarsSpace explorationSpace (punctuation)Mars Exploration ProgramArtificial intelligenceSystems engineeringRobotAerospace engineeringSatelliteComputer scienceNASA Deep Space NetworkAeronauticsSpace technologyGeosynchronous orbitEngineeringAstrobiologySpacecraft
DOInot available

Abstract

fetched live from OpenAlex

For many years, space robotics has been a key element of the Canadian Space Program with over $2B of total investment. Robotic arms designed and built by MDA are used on virtually all flights of the Space Shuttle and are operating on the International Space Station. MDA is also providing sophisticated robotic systems for autonomous satellite rendezvous and servicing missions including the robotic repair of the Hubble Space Telescope. Building upon this strong heritage, MDA is setting its sights beyond Earth orbit toward the exploration of the moon, Mars, and beyond. Strategic technologies are being developed to allow Canada to continue as a leader in space robotics, and provide the critical robotic systems for this next chapter in humanity’s exploration of space. This paper provides an overview of the work ongoing at MDA to continue to advance space robotic capabilities ranging from rovers to flight experiments to complete mission designs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.267
Teacher spread0.237 · 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 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

Citations3
Published2005
Admission routes1
Has abstractyes

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