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Record W4295599277 · doi:10.1029/2022gl099848

Tropopause‐Level NO<sub>x</sub> in the Asian Summer Monsoon

2022· article· en· W4295599277 on OpenAlexafffund
Kimberlee Dubé, William J. Randel, Adam Bourassa, D. A. Degenstein

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
FundersNational Aeronautics and Space AdministrationCanadian Space AgencyNational Science Foundation
KeywordsTropopauseClimatologyEast Asian MonsoonMonsoonEnvironmental scienceAtmospheric sciencesMeteorologyGeologyGeographyStratosphere

Abstract

fetched live from OpenAlex

Abstract Deep convection within the Asian summer monsoon (ASM) transports surface level air into the upper troposphere‐lower stratosphere (UTLS). This work aims to understand the distribution of NO 2 , NO, and NO x in the UTLS ASM anticyclone from satellite measurements. Observations of NO 2 from the Optical Spectrograph and InfraRed Imager System, the Atmospheric Chemistry Experiment ‐ Fourier Transform Spectrometer (ACE‐FTS), and the Stratospheric Aerosol and Gas Experiment III on the International Space Station are considered. The PRATMO photochemical box model is used to quantify the NO x photochemistry, and to derive the NO x concentration using OSIRIS NO 2 and O 3 observations. The satellite data show a relative minimum in NO 2 over the ASM in the summer months, while the corresponding NO and NO x anomalies are elevated, mainly due to low O 3 and cold temperatures within the ASM. The observations within the ASM show reasonable agreement to simulations from the Whole Atmosphere Community Climate Model.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.286
Teacher spread0.234 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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