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Record W2022639485 · doi:10.1016/j.crte.2009.10.012

Spatial observation of the ozone layer

2010· article· en· W2022639485 on OpenAlexaboutno aff
Sophie Godin‐Beekmann

Bibliographic record

VenueComptes Rendus Géoscience · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzoneOzone layerOzone depletionEnvironmental scienceAtmospheric sciencesAtmospheric chemistryStratosphereOzone Monitoring InstrumentSatelliteMontreal ProtocolMeteorologyGeographyGeologyPhysics

Abstract

fetched live from OpenAlex

This article provides an overview of the various satellite instruments, which have been used to observe stratospheric ozone and other chemical compounds playing a key role in stratospheric chemistry. It describes the various instruments that have been launched since the late 1970s for the measurement of total ozone column and ozone vertical profile, as well as the major satellite missions designed for the study of stratospheric chemistry. Since the discovery of the ozone hole in the early 1980s, spatial ozone measurements have been widely used to evaluate and quantify the spatial extension of polar ozone depletion and global ozone decreasing trends as a function of latitude and height. Validation and evaluation of satellite ozone data have been the subject of intense scientific activity, which was reported in the various ozone assessments of the state of the ozone layer published after the signature of the Montreal protocol. Major results, based on satellite observations for the study of ozone depletion at the global scale and chemical polar ozone loss, are provided. The use of satellite observations for the validation of chemistry climate models that simulate the recovery of the ozone layer and in data assimilation is also described.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.225
Teacher spread0.199 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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