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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 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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0440.022

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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