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Record W2154350770 · doi:10.5194/amt-4-965-2011

Fast NO <sub>2</sub> retrievals from Odin-OSIRIS limb scatter measurements

2011· article· en· W2154350770 on OpenAlexafffund
Adam Bourassa, C. A. McLinden, Christopher E. Sioris, S. Brohede, A. F. Bathgate, E. J. Llewellyn, D. A. Degenstein

Bibliographic record

VenueAtmospheric measurement techniques · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersCanadian Space AgencyTekesCentre National d’Etudes Spatiales
KeywordsOsirisSpectrographRemote sensingWavelengthSatelliteSpectral bandsRange (aeronautics)Environmental sciencePhysicsOpticsMaterials scienceSpectral lineGeography

Abstract

fetched live from OpenAlex

Abstract. The feasibility of retrieving vertical profiles of NO2 from space-based measurements of limb scattered sunlight has been demonstrated using several different data sets since the 1980's. The NO2 data product routinely retrieved from measurements made by the Optical Spectrograph and InfraRed Imaging System (OSIRIS) instrument onboard the Odin satellite uses a spectral fitting technique over the 437 to 451 nm range, over which there are 36 individual wavelength measurements. In this work we present a proof of concept technique for the retrieval of NO2 using only 4 of the 36 OSIRIS measurements in this wavelength range, which reduces the computational cost by almost an order of magnitude. The method is an adaptation of a triplet analysis technique that is currently used for the OSIRIS retrievals of ozone at Chappuis band wavelengths. The results obtained are shown to be in very good agreement with the spectral fit method, and provide an important alternative for applications where the computational burden is very high. Additionally this provides a baseline for future instrument design in terms of cost effectiveness and reducing spectral range requirements.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.056
GPT teacher head0.214
Teacher spread0.158 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations41
Published2011
Admission routes2
Has abstractyes

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