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Record W4396690610 · doi:10.15278/isms.2023.6836

IN SITU AND REMOTE SENSING OF SULFATE AEROSOLS

2023· article· en· W4396690610 on OpenAlexaff
Dylan English, Murphy Daniel Boone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRemote sensingOccultationStratosphereAerosolSpectrometerAtmosphere (unit)Environmental scienceRadianceSatelliteSulfateMie scatteringAtmospheric sciencesAtmospheric compositionEarth's energy budgetScatteringMaterials scienceOpticsMeteorologyPhysicsRadiationLight scatteringGeologyAstronomy

Abstract

fetched live from OpenAlex

Stratospheric sulfate aerosols play a crucial role in the physical and chemical processes in the Earth's atmosphere.They have a strong impact on climate by absorbing and scattering both incoming and outgoing radiation.The Atmospheric Chemistry Experiment Fourier Transform Spectrometer is recording infrared transmittance spectra of the Earth's limb from low Earth orbit (solar occultation).These infrared spectra provide accurate measurements of sulfate aerosol composition 1 , but have difficulty providing information on physical properties such as the particle size distribution.In contrast, optical extinction measurements, such as from the SAGE III/ISS instrument on the International Space Station, provide information on physical properties, but little data on composition.In situ measurements, made from aircraft, with a mass spectrometer and laser light scattering provide some information on composition and reliable information on physical properties 2 .By combining the information from satellite observations and in situ measurements, a more complete characterization of stratospheric sulfate aerosols has been obtained.

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 score0.003
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.229
Teacher spread0.212 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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