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Comment on egusphere-2023-773

2023· peer-review· en· W4380624762 on OpenAlexaff
Vitali Fioletov, Xiaoyi Zhao, Ihab Abboud, Michael Brohart, Akira Ogyu, Reno Sit, Sum Chi Lee, Irina Petropavlovskikh, Koji Miyagawa, B. J. Johnson, Patrick Cullis, John A. Booth, Glen McConville, C. T. McElroy

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsYork UniversityEnvironment and Climate Change Canada
FundersOffice of Polar ProgramsNational Science Foundation
KeywordsStratospherePolar vortexEnvironmental scienceAtmospheric sciencesMeteorologyClimatologyOzoneGeographyGeology

Abstract

fetched live from OpenAlex

Abstract. The Antarctic polar vortex creates unique chemical and dynamical conditions when the stratospheric air over Antarctica is isolated from the rest of the stratosphere. As a result, stratospheric ozone within the vortex remains largely unchanged for a five-month period from April until late August when the sunrise and extremely cold temperatures create favorable conditions for rapid ozone loss. Such prolonged stable conditions within the vortex make it possible to estimate the total ozone levels there from sparse wintertime ozone observations at the South Pole. The available records of focused Moon (FM) observations by Dobson and Brewer spectrophotometers at the Amundsen-Scott South Pole Station (for the periods 1964–2022 and 2008–2022, respectively) as well as integrated ozonesonde profiles (1986–2022) and MERRA-2 reanalysis data (1980–2022) were used to estimate the total ozone variability and long-term changes over the South Pole. Comparisons with MERRA-2 reanalysis data for the period 1980–2022 demonstrated that the uncertainties of Dobson and Brewer daily mean FM values are about 2.5 %–4 %. Wintertime (April–August) MERRA-2 data have a bias with Dobson data of -8.5 % in 1980–2004 and 1.5 % in 2005–2022. The mean difference between wintertime Dobson and Brewer data in 2008–2022 was about 1.6 %; however, this difference can be largely explained by various systematic errors in Brewer data. The wintertime ozone values over the South Pole during the last 20 years were about 12 % below the pre-1980s level, i.e., the decline there was nearly twice larger than that over southern midlatitudes. It is probably the largest long-term ozone decline aside from the springtime Antarctic ozone depletion. While wintertime ozone decline over the pole has hardly any impact on the environment, it can be used as an indicator to diagnose the state of the ozone layer, particularly because it requires data from only one station. Dobson and ozonesonde data after 2001 show a small positive, but not statistically significant, trend in ozone values of about 1.5% per decade that is in line with the trend expected from the concentration of the ozone depleting substances in the stratosphere.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.354
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0180.009
Insufficient payload (model declined to judge)0.3540.260

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 head0.038
GPT teacher head0.275
Teacher spread0.237 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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