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Record W4399528856 · doi:10.1038/s41558-024-02038-7

A decrease in radiative forcing and equivalent effective chlorine from hydrochlorofluorocarbons

2024· article· en· W4399528856 on OpenAlexaboutno aff
Luke M. Western, J. S. Daniel, Martin K. Vollmer, Scott Clingan, Molly Crotwell, Paul J. Fraser, Anita L. Ganesan, B. D. Hall, Christina M. Harth, Paul B. Krummel, Jens Mühle, Simon O’Doherty, Peter K. Salameh, Kieran Stanley, Stefan Reimann, Isaac Vimont, Dickon Young, Matthew Rigby, Ray F. Weiss, Ronald G. Prinn, S. A. Montzka

Bibliographic record

VenueNature Climate Change · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersBundesamt für UmweltHorizon 2020 Framework ProgrammeNuclear Safety and Security CommissionCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorNatural Environment Research CouncilCommonwealth Scientific and Industrial Research OrganisationUniversity of BristolEuropean CommissionNational Oceanic and Atmospheric AdministrationSight Research UKNational Aeronautics and Space Administration
KeywordsMontreal ProtocolRadiative forcingOzone layerOzone depletionEnvironmental scienceAtmospheric sciencesForcing (mathematics)OzoneGreenhouse gasChlorineRadiative transferGreenhouse effectClimate changeChemistryEnvironmental chemistryGlobal warmingEcologyPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract The Montreal Protocol and its successive amendments have been successful in curbing emissions of ozone-depleting substances and potent greenhouse gases via production/consumption controls. Here we show that the radiative forcing and equivalent effective chlorine from hydrochlorofluorocarbons has decreased from 61.75 mW m − 2 and 321.69 ppt, respectively, since 2021, 5 years before the most recent projected decrease. This important milestone demonstrates the benefits of the Protocol for mitigating climate change and stratospheric ozone layer loss.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.865

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 integrity0.0000.001
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 head0.013
GPT teacher head0.249
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 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

Citations21
Published2024
Admission routes1
Has abstractyes

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