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Record W4283156572 · doi:10.5194/amt-2022-133

An Improved OSIRIS NO <sub>2</sub> Profile Retrieval in the UTLS and Intercomparison with ACE-FTS and SAGE III/ISS

2022· preprint· en· W4283156572 on OpenAlexafffund
Kimberlee Dubé, Daniel Zawada, Adam Bourassa, D. A. Degenstein, William J. Randel, D. E. Flittner, Patrick E. Sheese, Kaley A. Walker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencySwedish National Space Agency
KeywordsOsirisStratosphereTroposphereAtmospheric sciencesEnvironmental scienceLatitudeAltitude (triangle)MeteorologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

Abstract. The v7.2 NO2 retrieval for the Optical Spectrograph and InfraRed Imager System (OSIRIS) was designed to improve sensitivity in the Upper Troposphere-Lower Stratosphere (UTLS) and to reduce an observed low bias in the previous version, v6.0. The details of this retrieval are described, and then the data are compared to coincident NO2 profiles from the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) and the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS). The the PRATMO photochemical box model was used to account for differences in the measurement times of the instruments: all datasets were scaled to the same local solar time of 12:00 pm. Coincident ACE-FTS and OSIRIS NO2 measurements agree within 20 % throughout much of the stratosphere. Coincident SAGE III/ISS and OSIRIS NO2 measurements also agree within 20 %, with OSIRIS biased low at all altitudes and latitudes. The ACE-FTS, OSIRIS, and SAGE III-ISS NO2 monthly zonal mean data show very similar variability in time at most altitude and latitudes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 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

Citations8
Published2022
Admission routes2
Has abstractyes

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