MétaCan
Menu
Back to cohort
Record W1968091164 · doi:10.1029/2005gl024031

Future tropospheric ozone simulated with a climate‐chemistry‐biosphere model

2005· article· en· W1968091164 on OpenAlexaff
Didier Hauglustaine, Juliette Lathière, Sophie Szopa, Gerd Folberth

Bibliographic record

VenueGeophysical Research Letters · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
Fundersnot available
KeywordsBiosphereBiosphere modelEnvironmental scienceTropospheric ozoneAtmospheric sciencesOzoneTroposphereClimate modelAtmospheric chemistryClimatologyClimate changeMeteorologyGeologyOceanographyGeographyEcology

Abstract

fetched live from OpenAlex

A climate‐chemistry model and a biogenic emission model are used to investigate the relative impact of anthropogenic and biogenic emissions of ozone precursors and global warming on the evolution of ozone in 2100. A warmer and wetter climate leads to enhanced ozone photochemical destruction in the lower troposphere, a more intense Brewer‐Dobson circulation in the stratosphere and a lightning NO x emission increased from 5 to 7.5 Tg(N)/yr. Over Europe and the eastern US climate change locally causes surface ozone to increase because of enhanced PAN thermal decomposition and more stagnant meteorological conditions. The global and annual mean OH concentration remains quite stable and the methane lifetime is unchanged in the future. Increased biogenic emissions contribute by 30–50% to surface ozone summer formation in northern continental regions. The feedback of climate change and of biogenic emissions increases the 2000 to 2100 tropospheric ozone radiative forcing by 12%, up to a global mean value of 0.58 Wm −2 .

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations104
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicAtmospheric chemistry and aerosolsFrench-language works237,207