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Record W3213990579 · doi:10.1002/essoar.10503234.1

The Arctic Ozone Hole in March 2020 and Its Seasonal Prediction in CFSv2: A Comparative Study with the 1997 and 2011 Arctic Ozone Holes

2020· preprint· en· W3213990579 on OpenAlexaboutno aff
Jian Rao, Chaim I. Garfinkel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzonePolar vortexArcticPredictabilityClimatologyOzone depletionEnvironmental scienceAtmospheric sciencesThe arcticMeteorologyOceanographyGeographyGeologyPhysics

Abstract

fetched live from OpenAlex

Using reanalysis data, observations, and seasonal forecasts, the March Arctic ozone hole events in 1997, 2011, and 2020 and their predictability are compared. All of the three ozone hole events were accompanied by an extremely strong and cold polar vortex. The shape and centroid of the ozone holes are mainly controlled by the simultaneous polar vortex. The March 2020 ozone hole was displaced towards Canada and Greenland, the March 2011 ozone low was evenly distributed over the North Pole, while the 1997 ozone hole was displaceds toward Arctic Russia. The predictability of the 2011 ozone hole event is longer (1–2 months) than the other two (~1 month) possibly due to La Niña and Quasi-Biennial westerly winds, favorable for formation of a strong polar vortex. Surprisingly, an empirical model using a substitute index to forecast the Arctic ozone might be as skillful as the general circulation model with a chemistry module.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.028
GPT teacher head0.245
Teacher spread0.217 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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