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Record W2580193130 · doi:10.1109/tgrs.2016.2645614

Preflight Spectral Calibration of the Orbiting Carbon Observatory 2

2017· article· en· W2580193130 on OpenAlexaff
Richard A. Lee, C. O’Dell, Debra Wunch, Coleen M. Roehl, G. B. Osterman, Jean-François Blavier, Robert Rosenberg, Lars Chapsky, Christian Frankenberg, Sarah L. Hunyadi-Lay, B. Fisher, David M. Rider, David Crisp, Randy Pollock

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersNational Aeronautics and Space Administration
KeywordsSpectrometerRemote sensingObservatoryCalibrationSolar observatoryOpticsDispersion (optics)Spectral lineSpectral resolutionEnvironmental sciencePhysicsGeologyAstronomy

Abstract

fetched live from OpenAlex

This paper describes the preflight spectral calibration methods and results for the Orbiting Carbon Observatory 2 (OCO-2), following the approach developed for the first OCO. The instrument line shape (ILS) function and dispersion parameters were determined through laser-based spectroscopic measurements, and then further optimized by comparing solar spectra recorded simultaneously on the ground by the OCO-2 flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The resulting ILS profiles and dispersion parameters, when applied to the FTS solar data, showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% RMS, satisfying the preflight spectral calibration accuracy requirement of <;0.25% RMS. Specific changes to the OCO-2 instrument and calibration process, compared to the original OCO, include stray-light protection; improved laser setup; increased spectral sampling; enhanced data screening, and incremental improvements in the ILS, dispersion, and FTS optimization analyses.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.654

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 studies0.0010.001
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations52
Published2017
Admission routes1
Has abstractyes

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