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Record W2884775273 · doi:10.1029/2018gl078596

On the Role of Heterogeneous Chemistry in Ozone Depletion and Recovery

2018· article· en· W2884775273 on OpenAlexaboutno aff
Catherine Wilka, Kasturi Shah, Kane A. Stone, Susan Solomon, Douglas E. Kinnison, Michael Mills, Anja Schmidt, Ryan R. Neely

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersGoddard Space Flight CenterSight Research UKOffice of ScienceNational Aeronautics and Space AdministrationNatural Environment Research CouncilU.S. Department of EnergyNational Center for Atmospheric ResearchNational Science Foundation
KeywordsOzoneOzone depletionVolcanoAerosolStratosphereOzone layerAtmospheric sciencesAtmospheric chemistryEnvironmental scienceTrace gasClimatologyMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract We demonstrate that identification of stratospheric ozone changes attributable to ozone depleting substances and actions taken under the Montreal Protocol requires evaluation of confounding influences from volcanic eruptions. Using a state‐of‐the‐art chemistry‐climate model, we show that increased stratospheric aerosol loading from volcanic eruptions after 2004 impeded the rate of ozone recovery post‐2000. In contrast, eruptions increased ozone loss rates over the depletion era from 1980 to 1998. We also present calculations without any aerosol chemistry to isolate contributions from gas‐phase chemistry alone. This study reinforces the need for accurate information regarding stratospheric aerosol loading when modeling ozone changes, particularly for the challenging task of accurately identifying the early signs of ozone healing distinct from other sources of variability.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.251
Teacher spread0.235 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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