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Record W2033636508 · doi:10.1117/12.458123

CrIS raw data records (level 0) to sensor data records (level 1b) processing

2002· article· en· W2033636508 on OpenAlexaff
Robert H. Poulin, Stéphane Lantagne, Yvan Dutil, Steve Levesque, François Châteauneuf

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsABB (Canada)
Fundersnot available
KeywordsRadianceRemote sensingMichelson interferometerComputer scienceDepth soundingContext (archaeology)InterferometrySatelliteAtmospheric soundingEnvironmental scienceData processingOpticsPhysicsGeologyDatabase

Abstract

fetched live from OpenAlex

The Cross-track Infrared Sounder (CrIS) is part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) series of polar-orbiting spacecrafts. The CrIS sensor forms a key component of the larger Cross-track Infrared/Microwave Sounding Suite (CrIMSS) and is intended to operate within the context of the CrIMSS architecture. The CrIS instrument is a Michelson interferometer infrared sounder covering the spectral range of approximately 3.9 to 15.4 microns. CrIS provides cross-track measurements of scene radiance to allow the calculation of temperature and moisture vertical distributions in the Earth's atmosphere. We present sensor data record (SDR) level 1B algorithms that are needed on ground in order to produce meaningful data meeting all requirements of the NPOESS CrIS instrument. Level 1B data are made of geolocated, radiometrically and spectrally (spatial frequency) calibrated spectra with annotated quality indicators. CrIS SDR algorithms include, among others, radiometric calibration with phase error correction, interferometer fringe count error handling and correction of the instrument line shape (ILS) distortion.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.001
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 head0.083
GPT teacher head0.274
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 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

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCalibration and Measurement TechniquesFrench-language works237,207