MétaCan
Menu
← Back to cohort
Record W2042796992 · doi:10.1117/12.547899

Laser imaging sensor system for on-orbit space shuttle inspection

2004· article· en· W2042796992 on OpenAlexaff
Dennis Gregoris, Arkady Ulitsky, Dennis Vit, Peter Dorcas, George V. Bailak, Jeffrey W. Tripp, Ross Gillett, Chris Woodland, Robert D. Richards, C. Sallaberger

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsSpace ShuttleLidarRemote sensingComputer scienceInternational Space StationOrbit (dynamics)ScannerGeosynchronous orbitSatelliteArtificial intelligenceAerospace engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

The Shuttle Inspection Lidar (SIL) system is a derivative of a scanning lidar system being developed by MD Robotics and Optech. It incorporates a lidar, a camera, lights and video communications systems. The SIL is designed to meet the specific requirements for the on-orbit inspection and measurement of the Space Shuttle leading edge Reinforced-Carbon Carbon (RCC) and Thermal Protection System (TPS). The SIL has a flexible electrical and mechanical interface that enables it to be mounted on different locations including the Shuttle Remote Manipulator System (SRMS, Canadarm), and the Space Station Remote Manipulator System (SSRMS) on the International Space Station (ISS). This paper describes the SIL system and the specifications of the imaging lidar scanner system, and discusses the application of the SIL for on-orbit shuttle inspection using the on-orbit SRMS. Ground-based measurements of the shuttle TPS taken by a terrestrial version of the imager are also presented.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.005

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 designBench or experimental
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

Citations4
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE→Same topicSpace Satellite Systems and Control→French-language works237,207→