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Record W4300179820 · doi:10.5194/acp-5-131-2005

Vortex-averaged Arctic ozone depletion in the winter 2002/2003

2005· article· en· W4300179820 on OpenAlexaff
B. M. Knudsen, Søren Bo Andersen, A. Benesova, Geir Braathen, J. Davies, H. De Backer, Michael Gerding, M. Gil, B. Henchoz, H. Kelder, Rigel Kivi, David Moore, G. Peters, P. Skřivánková, René Stübi, Timo Turunen, G. Vaughan, P. Viatte, Peter von der Gathen, I. Zaitcev

Bibliographic record

VenueAtmospheric chemistry and physics · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersFifth Framework Programme
KeywordsOzone depletionOzoneAtmospheric sciencesArcticEnvironmental scienceClimatologyVortexPolar vortexThe arcticMeteorologyOceanographyPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract. A total ozone depletion of 68±7 Dobson units between 380 and 525K from 10 December 2002 to 10 March 2003 is derived from ozone sonde data by the vortex-average method, taking into account both diabatic descent of the air masses and transport of air into the vortex. When the vortex is divided into three equal-area regions, the results are 85±9DU for the collar region (closest to the edge), 52±5DU for the vortex centre and 68±7DU for the middle region in between centre and collar. Our results compare well with other studies: We find good agreement with ozone loss deduced from SAOZ data, with results inferred from POAM III observations and with results from tracer-tracer correlations using HF as the long-lived tracer. We find a higher ozone loss than that deduced by tracer-tracer correlations using CH4. We have made a careful comparison with Match results: The results were recalculated using a common time period, vortex edge definition and height interval. The two methods generally compare very well, except at the 475K level which exhibits an unexplained discrepancy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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)0.0060.000

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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.

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

Citations25
Published2005
Admission routes1
Has abstractyes

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