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Record W2947828759 · doi:10.1117/1.oe.58.5.054104

Calibration lamp design, characterization, and implementation for the Michelson Interferometer for Global High-Resolution Thermospheric Imaging instrument on the Ionospheric Connection satellite

2019· article· en· W2947828759 on OpenAlexaff
Kenneth D. Marr, William Morrow, C. M. Brown, Christoph R. Englert, John M. Harlander, Angel Cerrato

Bibliographic record

VenueOptical Engineering · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsBarrie Urology Group
FundersNational Aeronautics and Space Administration
KeywordsMichelson interferometerCalibrationOpticsAstronomical interferometerInterferometryRemote sensingPhysicsSatellite

Abstract

fetched live from OpenAlex

We describe the design and ground-based performance of the two-color calibration lamp for the Michelson Interferometer for Global High-Resolution Thermospheric Imaging (MIGHTI) instrument on the NASA Ionospheric Connection (ICON) satellite. The calibration lamp assembly contains radio frequency excited krypton and neon lamps, which generate emission lines at 557 and 630 nm, respectively, and which are used to monitor thermal drifts in the two MIGHTI Doppler asymmetric spatial heterodyne interferometers. The lamps are coupled to two mixed optical fiber bundles that deliver the calibration signals to the two MIGHTI optical units. The assembly starts reliably, consumes <8 W, and has passed environmental testing for the ICON satellite. The total mass of the lamp assembly is 1.8 kg. Special features of the assembly and its implementation are described along with results of life tests.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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
GenreMethods

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

Citations8
Published2019
Admission routes1
Has abstractyes

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