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Record W2289605592 · doi:10.26021/7543

The Chemical Sensitivity of Stratospheric Ozone to N₂O and CH₄ through the 21st century

2012· article· en· W2289605592 on OpenAlexaboutno aff
Laura E. Revell

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzone layerOzoneAtmospheric sciencesEnvironmental scienceSensitivity (control systems)Ozone depletionAtmospheric chemistryStratosphereMeteorologyAstrobiologyClimatologyGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Through the 21st century, global-mean stratospheric ozone abundances are projected to increase due to decreasing chlorine and bromine concentrations (as a consequence of the Montreal Protocol for Substances that Deplete the Ozone Layer), and continued CO₂-induced cooling of the stratosphere. Along with CO₂, anthropogenic emissions of the greenhouse gases N₂O and CH₄ are projected to increase, thus increasing their atmospheric concentrations. Consequently, reactive nitrogen species produced from N₂O and reactive hydrogen species produced from CH₄ are expected to play an increasingly important role in determining stratospheric ozone concentrations. Chemistry-climate model simulations were performed using the NIWA-SOCOL (National Institute of Water and Atmospheric Research - SOlar Climate Ozone Links) model, which tracks the contributions to ozone loss from a prescribed set of catalytic cycles, including the ozone-depleting nitrogen and hydrogen cycles, over latitude, longitude, pressure and time. The results provide a comprehensive picture of how stratospheric ozone may evolve through the 21st century under a range of greenhouse gas emissions scenarios, and quantitatively extend concepts that had previously been understood only qualitatively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.336
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.223
Teacher spread0.205 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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