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Record W4240894711 · doi:10.5194/amtd-3-5343-2010

SCIAMACHY stratospheric aerosol extinction profile retrieval

2010· preprint· en· W4240894711 on OpenAlexaff
Ghassan Taha, Didier Rault, Robert Loughman, Adam Bourassa, Christian von Savigny

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
FundersUniversität BremenNational Aeronautics and Space Administration
KeywordsSCIAMACHYAerosolEnvironmental scienceOccultationRemote sensingStandard deviationMeteorologyAtmospheric sciencesGeologyTroposphereGeographyMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Abstract. The Ozone Mapper and Profiler Suite Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone and aerosol profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol profiles with accuracy comparable to other instruments. The retrieved aerosol profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.229
Teacher spread0.214 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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