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Record W1987945790 · doi:10.5194/amt-4-1319-2011

BrO vertical distributions from SCIAMACHY limb measurements: comparison of algorithms and retrieval results

2011· article· en· W1987945790 on OpenAlexaff
Alexei Rozanov, S. Kühl, Adrian Doicu, C. A. McLinden, Jānis Puķīte, H. Bovensmann, John P. Burrows, T. Deutschmann, M. Dorf, F. Goutail, K. Grunow, F. Hendrick, Marc von Hobe, S. Hrechanyy, Günter Lichtenberg, Klaus Pfeilsticker, J. P. Pommereau, Michel Van Roozendaël, F. Stroh, Thomas Wagner

Bibliographic record

VenueAtmospheric measurement techniques · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersEuropean Commission
KeywordsSCIAMACHYRadiative transferEnvironmental scienceRemote sensingAlgorithmAtmospheric radiative transfer codesMeteorologyComputer scienceZenithSensitivity (control systems)Atmospheric sciencesTroposphereGeologyPhysicsOpticsEngineering

Abstract

fetched live from OpenAlex

Abstract. This study presents two scientific and one operational retrieval algorithms used to obtain vertical distributions of bromine monoxide (BrO) from observations of the scattered solar light performed by the SCIAMACHY instrument in limb viewing geometry. The study begins with a discussion of the theoretical basis of all algorithms followed by an investigation of the retrieval sensitivity. Simulations with three different radiative transfer models allow us to analyze influence of the forward model implementation upon the retrieval results. By means of synthetic retrievals we analyze major sources of uncertainties in the resulting BrO profiles such as different BrO cross sections, their temperature dependence, and stratospheric aerosols. Finally, the reliability of SCIAMACHY BrO profile retrievals is demonstrated comparing results from different algorithms to each other and to balloon-borne observations.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
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)0.0010.000

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 head0.092
GPT teacher head0.270
Teacher spread0.178 · 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 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

Citations87
Published2011
Admission routes1
Has abstractyes

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