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Record W4234548709 · doi:10.5194/essd-2020-342

Overview and update of the SPARC Data Initiative: Comparison ofstratospheric composition measurements from satellite limbsounders

2020· preprint· en· W4234548709 on OpenAlexafffund
Michaela I. Hegglin, Susann Tegtmeier, J. D. Anderson, Adam Bourassa, S. Brohede, D. A. Degenstein, L. Froidevaux, B. Funke, J. C. Gille, Yasuko Kasai, E. Kyrölä, J. D. Lumpe, D. Murtagh, Jessica L. Neu, Kristell Pérot, Ellis E. Remsberg, Alexei Rozanov, Matthew Toohey, T. von Clarmann, Kaley A. Walker, Ray H. J. Wang, Carlo Arosio, Robert Damadeo, R. A. Fuller, G. Lingenfelser, Christopher McLinden, Diane Pendlebury, Chris Roth, Niall J Ryan, Christopher E. Sioris, Lesley Smith, Katja Weigel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change CanadaUniversity of TorontoUniversity of Saskatchewan
FundersJapan Aerospace Exploration AgencyNatural Environment Research CouncilScheme for Promotion of Academic and Research CollaborationUniversität BremenSwedish National Space AgencyBundesministerium für Wirtschaft und EnergieCanadian Foundation for Climate and Atmospheric SciencesNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyJet Propulsion LaboratoryDeutsche Forschungsgemeinschaft
KeywordsAtmospheric compositionTrace gasTroposphereEnvironmental scienceSatelliteAtmospheric sciencesLatitudeMeteorologyStratosphereAerosolTRACE (psycholinguistics)ClimatologyAtmospheric chemistrySuiteAtmosphere (unit)OzoneGeologyGeographyAerospace engineering

Abstract

fetched live from OpenAlex

Abstract. The SPARC Data Initiative (SPARC, 2017) performed the first comprehensive assessment of currently available stratospheric composition measurements obtained from an international suite of space-based limb sounders. The initiative's main objectives were (1) to assess the state of data availability, (2) to compile vertically resolved, monthly zonal mean trace gas and aerosol climatologies, and (3) to perform a detailed inter-comparison of these climatologies, summarising useful information and highlighting differences among datasets. The vertically-resolved climatologies of 26 different atmospheric constituents extending over the region from the upper troposphere to the lower mesosphere (300–0.1 hPa) are provided on a common latitude-pressure grid and include most major long-lived trace gases (O3, H2O, N2O, CH4, CCl3F, and CCl2F2), transport tracers (HF, SF6, HCl, CO, HNO3, NOy), and shorter-lived trace gases important to stratospheric chemistry including nitrogens (NO, NO2, NOx, N

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)0.010
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.011
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.229
GPT teacher head0.309
Teacher spread0.079 · 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
Published2020
Admission routes2
Has abstractyes

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