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Record W2336137699 · doi:10.1039/9781849733182-00190

Impact of Polar Ozone Loss on the Troposphere

2011· book-chapter· en· W2336137699 on OpenAlexaff
Nathan P. Gillett, Seung Woo Son

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsMcGill UniversityUniversity of VictoriaEnvironment and Climate Change Canada
Fundersnot available
KeywordsTroposphereOzone depletionPolar vortexClimatologyAtmospheric sciencesEnvironmental scienceStratosphereSouthern HemisphereArcticNorthern HemisphereOzonePolar nightArctic geoengineeringTropospheric ozoneOceanographySea iceGeologyCryosphereGeographyAntarctic sea iceMeteorology

Abstract

fetched live from OpenAlex

The chapter reviews the evidence for the influence of Antarctic and Arctic ozone changes on the troposphere and ocean, and the mechanisms underlying this coupling. Antarctic ozone depletion has driven a cooling and strengthening of the polar vortex in austral spring. Models and observations show that this change has also been associated with a strengthening and poleward shift of the midlatitude westerly winds in the Southern Hemisphere troposphere in austral summer, as well as a cooling of the Antarctic troposphere. Antarctic ozone depletion has been associated with summer trends in surface temperature over Antarctica, precipitation over the southern hemisphere, Southern Ocean circulation, and ocean-atmosphere fluxes of carbon dioxide over the Southern Ocean. Both radiative and dynamical mechanisms are thought to be involved in communicating the response to stratospheric ozone depletion to the troposphere. In the Arctic there is some modeling evidence suggestive of a link between ozone depletion and tropospheric circulation changes, but due to the weaker ozone trends and larger internal variability, no such relationship has been identified in the observations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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 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

Citations2
Published2011
Admission routes1
Has abstractyes

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