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Record W2253677266 · doi:10.1002/2015gl065702

The changing ozone depletion potential of N<sub>2</sub>O in a future climate

2015· article· en· W2253677266 on OpenAlexaboutno aff
Laura E. Revell, Fiona Tummon, R. J. Salawitch, Andrea Stenke, Thomas Peter

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersEidgenössische Technische Hochschule ZürichNational Aeronautics and Space Administration
KeywordsOzone depletionOzoneOzone layerStratosphereNitrous oxideGreenhouse gasAtmospheric sciencesEnvironmental scienceNOxNitrogenAtmospheric chemistryEnvironmental chemistryMontreal ProtocolChemistryGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Nitrous oxide (N2O), which decomposes in the stratosphere to form nitrogen oxides (NOx), is currently the dominant anthropogenic ozone‐depleting substance emitted. Ozone depletion potentials (ODPs) of specific compounds, commonly evaluated for present‐day conditions, were developed for long‐lived halocarbons and are used by policymakers to inform decision‐making around protection of the ozone layer. However, the effect of N2O on ozone will evolve in the future due to changes in stratospheric dynamics and chemistry induced by rising levels of greenhouse gases. Despite the fact that NOx‐induced ozone loss slows with increasing concentrations of CO2 and CH4, we show that ODPN2O for year 2100 varies under different scenarios and is mostly larger than ODPN2O for year 2000. This occurs because the traditional ODP approach is tied to ozone depletion induced by CFC‐11, which is also sensitive to CO2 and CH4. We therefore suggest that a single ODP for N2O is of limited use.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Citations64
Published2015
Admission routes1
Has abstractyes

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