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Record W2059852281 · doi:10.1029/2008jd011333

Short‐term production and synoptic influences on atmospheric <sup>7</sup>Be concentrations

2009· article· en· W2059852281 on OpenAlexaff
Ilya Usoskin, Christy V. Field, Gavin A. Schmidt, Ari‐Pekka Leppänen, Ala Aldahan, G. A. Kovaltsov, Göran Possnert, R. Kurt Ungar

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsHealth Canada
FundersSuomalainen TiedeakatemiaAcademy of FinlandNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsAtmosphere (unit)Environmental scienceAtmospheric sciencesRadionuclideClimatologyStratosphereMeteorologyGeologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Variations of the cosmogenic radionuclide 7 Be in the global atmosphere are driven by cooperation of processes of its production, air transports, and removal. We use a combination of the Goddard Institute for Space Studies ModelE and the OuluCRAC:7Be production model to simulate the variations in the 7 Be concentration in the atmosphere for the period from 1 January to 28 February 2005. This period features significant synoptic variability at multiple monitoring stations around the globe and spans an extreme solar energetic particle (SEP) event that occurred on 20 January. Using nudging from observed horizontal winds, the model correctly reproduces the overall level of the measured 7 Be concentration near ground and a great deal of the synoptic variability at timescales of 4 days and longer. This verifies the combined model of production and transport of the 7 Be radionuclide in the atmosphere. The impact of an extreme SEP event of January 2005 is seen dramatically in polar stratospheric 7 Be concentration but is small near the surface (about 2%) and indistinguishable given the amount of intrinsic variability and the uncertainties of the surface 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.694
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 teacher head, 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

Citations64
Published2009
Admission routes1
Has abstractyes

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