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Record W654788663

Canada’s Smallest Satellite: The Canadian Advanced Nanospace Experiment (CanX-1)

2002· article· en· W654788663 on OpenAlexfundaboutno aff
James Wells, Luke Stras, Tiger Jeans

Bibliographic record

VenueDigital Commons - USU (Utah State University) · 2002
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSatelliteAerospaceTracking (education)Space technologySpace (punctuation)Global Positioning SystemSatellite trackingSpace researchSpace industrySatellite broadcastingAeronauticsEngineeringComputer scienceAerospace engineeringTelecommunicationsOperating systemSociology
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Advanced Nanospace eXperiment (CanX) Program of the Space Flight Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS/SFL) is a Canadian first, allowing engineering researchers to test nano- and micro-scale devices rapidly and inexpensively in space. CanX is a “picosatellite” program for research and education, with graduate students leading the design, development, testing, and operations of Canada’s smallest satellites having a mass under 1 kg. The first UTIAS/SFL picosatellite, CanX-1, is scheduled for launch in early 2003 together with CubeSats from other university and industry developers. The objective of the CanX-1 mission is to verify the functionality of several novel electronic technologies in orbital space. This paper outlines the features, capabilities and performance of CanX-1, including horizon and star-tracking experiments using two CMOS imagers, active threeaxis magnetic stabilization, GPS-based position determination, and an ARM7 central processor.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.158
Teacher spread0.147 · 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

Citations23
Published2002
Admission routes2
Has abstractyes

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