MétaCan
Menu
Back to cohort
Record W3037544338 · doi:10.1029/2019gl086791

A Post‐2013 Dropoff in Total Ozone at a Third of Global Ozonesonde Stations: Electrochemical Concentration Cell Instrument Artifacts?

2020· article· en· W3037544338 on OpenAlexaff
Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, J. C. Witte, D. W. Tarasick, J. Davies, Holger Vömel, Gary A. Morris, Roeland Van Malderen, B. J. Johnson, Richard Querel, Henry B. Selkirk, René Stübi, H. G. J. Smit

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersGoddard Space Flight CenterNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsSatelliteEnvironmental scienceMeteorologyOzoneOzone depletionClimatologyAtmospheric sciencesRemote sensingGeographyGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract An international effort to improve ozonesonde data quality and to reevaluate historical records has made significant improvements in the accuracy of global network data. However, between 2014 and 2016, ozonesonde total column ozone (TCO; O 3 ) at 14 of 37 regularly reporting stations exhibited a sudden dropoff relative to satellite measurements. The ozonesonde TCO drop is 3–7% compared to satellite and ground‐based TCO, and 5–10% or more compared to satellite stratospheric O 3 profiles, compromising the use of recent data for trends, although they remain reliable for other uses. Hardware changes in the ozonesonde instrument are likely a major factor in the O 3 dropoff, but no single property of the ozonesonde explains the findings. The bias remains in recent data. Research to understand the dropoff is in progress; this letter is intended as a caution to users of the data. Our findings underscore the importance of regular ozonesonde data evaluation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.909

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.261
Teacher spread0.241 · 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 teacher head, 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

Citations90
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicAtmospheric Ozone and ClimateFrench-language works237,207