MétaCan
Menu
Back to cohort
Record W2017222718 · doi:10.1117/12.609448

I-SIL: features and capabilities of a long-range shuttle inspection characteristics lidar

2005· article· en· W2017222718 on OpenAlexaff
Ross Gillett, Éric Martin, Arkady Ulitsky

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsOptech (Canada)Canadian Space Agency
Fundersnot available
KeywordsLidarRange (aeronautics)Remote sensingComputer scienceSpace ShuttleEnvironmental scienceGeologyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

The tragic loss of the Space Shuttle Columbia and crew in 2003 has resulted in a requirement to inspect the Shuttle Thermal Protection System (TPS) on-orbit so that the crew may remain at the International Space Station (ISS) in the event of damage that might pose an unacceptable risk to their safe return. An instrumented inspection boom manipulated and operated from the Shuttle’s Canadarm will provide an interim solution for the initial flights. However, a longer term solution has been planned that will permit the required inspection to be performed from within the ISS through the ISS windows. This plan involves the Shuttle performing a pitching maneuver to expose the underside for inspection purposes as it approaches the ISS prior to docking. The central approach in this plan is for the ISS crew to photograph the Shuttle TPS through the ISS windows using high-definition cameras. As an augmentation to this plan, the ISS-based Shuttle Inspection Lidar, or I-SIL, is a proposed lidar instrument that will generate a 3D topographic surface of the Shuttle underside to enable rapid identification and volumetric analysis of tile damage to generate safety and repair data. This paper presents the mission requirements and derived requirements for I-SIL, analyzes specific details of the inspection requirements, and discusses various phases of operating scenarios. The conclusion of the paper outlines the current status of the proposed technology.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.812

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)0.0000.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207