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Record W2041317387 · doi:10.1007/s11214-014-0107-x

Fast Auroral Imager (FAI) for the e-POP Mission

2014· article· en· W2041317387 on OpenAlexafffund
L. L. Cogger, Andrew Howarth, A. W. Yau, Andrew White, Greg Enno, T. Trondsen, Don Asquin, Blair Gordon, Paul Marchand, Danny Siu‐Chun Ng, Greg Burley, M. Lessard, B. Sadler

Bibliographic record

VenueSpace Science Reviews · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMagellan Aerospace (Canada)University of Calgary
FundersCanadian Space AgencyNational Aeronautics and Space Administration
KeywordsNadirAirglowRemote sensingSatellitePhysicsField of viewImage resolutionOpticsGeologyAstronomy

Abstract

fetched live from OpenAlex

The Fast Auroral Imager (FAI) consists of two charge-coupled device (CCD) cameras: one to measure the 630 nm emission of atomic oxygen in aurora and enhanced night airglow; and the other to observe the prompt auroral emissions in the 650 to 1100 nm range. High sensitivity is realized through the combination of fast lens systems ( f /0.8) and CCDs of high quantum efficiency (>90 % max). The cameras have a common 26 degree field-of-view to provide nighttime images of about 650 km diameter from apogee at 1500 km. The near infrared camera provides up to two images of 0.1 s exposure per second with a spatial resolution of a few km when the camera is pointing in the nadir direction, making it suitable for studies of dynamic auroral phenomena. The 630-nm camera has been designed to provide one image of 0.5 s exposure every 30 seconds. Launch of the satellite occurred on September 29, 2013. Following a description of the instrument, sample auroral images are 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.007

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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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

Citations20
Published2014
Admission routes2
Has abstractyes

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