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Record W2023368487 · doi:10.1029/2000gl011404

Ozone destruction and production rates between spring and autumn in the Arctic stratosphere

2000· article· en· W2023368487 on OpenAlexaff
D. W. Fahey, R. S. Gao, L. A. Del Negro, E. R. Keim, S. R. Kawa, R. J. Salawitch, P. O. Wennberg, T. F. Hanisco, E. J. Lanzendorf, Katherine K. Perkins, Steven A. Lloyd, W. H. Swartz, M. H. Proffitt, J. J. Margitan, J. C. Wilson, R. M. Stimpfle, R. C. Cohen, C. T. McElroy, Christopher R. Webster, M. Loewenstein, J. W. Elkins, T. P. Bui

Bibliographic record

VenueGeophysical Research Letters · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsStratosphereOzoneAtmospheric sciencesArcticOzone depletionEnvironmental scienceSpring (device)The arcticClimatologyMeteorologyGeographyOceanographyGeologyPhysics

Abstract

fetched live from OpenAlex

In situ measurements of radical and long‐lived species were made in the lower Arctic stratosphere (18 to 20 km) between spring and early autumn in 1997. The measurements include O3, ClO, OH, HO2, NO, NO2, N2O, CO, and overhead O3. A photochemical box model constrained by these and other observations is used to compute the diurnally averaged destruction and production rates of O3 in this region. The rates show a strong dependence on solar exposure and ambient O3. Total destruction rates, which reach 19%/month in summer, reveal the predominant role of NOx and HOx catalytic cycles throughout the period. Production of O3 is significant only in midsummer air parcels. A comparison of observed O3 changes with destruction rates and transport effects indicates the predominant role of destruction in spring and an increased role of transport by early autumn.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.029
GPT teacher head0.279
Teacher spread0.250 · 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

Citations17
Published2000
Admission routes1
Has abstractyes

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