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Record W2087401943 · doi:10.1029/2006gl028901

Simulation of ozone loss in Arctic winter 2004/2005

2007· article· en· W2087401943 on OpenAlexfundno aff
Jens‐Uwe Grooß, Rolf Müller

Bibliographic record

VenueGeophysical Research Letters · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersCanadian Space AgencyEuropean Centre for Medium-Range Weather Forecasts
KeywordsStratosphereOzonePolar vortexOzone depletionAtmospheric sciencesOzone layerEnvironmental scienceVortexArcticTotal Ozone Mapping SpectrometerClimatologyMixing ratioPolarMeteorologyGeologyPhysicsOceanography

Abstract

fetched live from OpenAlex

We present simulations of stratospheric ozone depletion in the Arctic winter 2004/2005 by the Chemical Lagrangian Model of the Stratosphere (CLaMS). This winter is among the coldest on record with large observed ozone losses. It is also different from previously analyzed winters, as ozone mixing ratios within the polar vortex were not homogeneously distributed. The reason for the untypical ozone distribution is a second transport barrier that existed at the time of vortex formation. The simulations agree well with ozone measurements by the Fourier Transform Spectrometer (ACE‐FTS). The simulated vortex average column ozone loss between 380 and 550 K potential temperature (±1σ) was 69 ± 21 Dobson Units on 23 March. The simulated ozone loss is in approximate agreement with some published estimates, but is significantly lower than others. A possible reason for this difference is the inhomogeneous ozone distribution within the vortex which makes it more complicated to estimate of ozone loss.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.311
Teacher spread0.281 · 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

Citations43
Published2007
Admission routes1
Has abstractyes

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