MétaCan
Menu
Back to cohort
Record W4401786339 · doi:10.1117/12.3020037

LUVCam the Little Ultraviolet Camera: a low-cost high-performance UV CMOS camera

2024· article· en· W4401786339 on OpenAlexaff
A. Tohuvavohu, Mark Barnet, Shaojie Chen, Christopher J. Damaren, M. R. Drout, Julia Empey, Jean-Christophe Fronteddu, Braden Gail, Ajay Gill, Gavin Hay, Sarik Jeram, Christopher D. Matzner, Patrick Nkwari, Dhwanil Patel, Emma Seabrook, Mohamed M. Shaaban, Suresh Sivanandam, Jacob Taylor, Phil Van-Lane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of WaterlooUniversity of TorontoHerzberg Institute of Astrophysics
Fundersnot available
KeywordsUltravioletCMOSCMOS sensorComputer scienceOptoelectronicsMaterials science

Abstract

fetched live from OpenAlex

Astronomy-grade cameras with robust performance and heritage in the space environment have long been costly, substantially limiting capacity for space-based astronomy and creating a resource barrier to access. Additionally, ultraviolet observations have historically been limited by the low-sensitivity of most sensors in this wavelength range. The LUVCam program is designed to address both issues, providing a high-performance, low-cost, UV/optical camera system sufficiently capable to support a wide-array of space-based astronomy missions. LUVCam features a large format, low-noise, large pixel, and high quantum efficiency, commercial-off-the-shelf backside illuminated CMOS sensor, packaged with custom built readout electronics and thermomechanical structure. LUVCam is ITAR-free, and cheap to fabricate, opening up new opportunities for access to space telescopes. LUVCam has reached TRL 6, and has passed qualification testing for operation in low-earth orbit, with competitive performance from 200-900 nm. LUVCam is manifested for multiple near-term orbital missions, including a technology demonstration CubeSat, and a UV transient astronomy SmallSat.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicCCD and CMOS Imaging SensorsFrench-language works237,207