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Record W4393467907 · doi:10.5281/zenodo.6958560

data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"

2022· dataset· en· W4393467907 on OpenAlexaffabout
Sophie Godin‐Beekmann, Viktoria Sofieva, Irina Petropavlovskikh, Peter Effertz, G. Ancellet, D. A. Degenstein, Daniel Zawada, L. Froidevaux, S. M. Frith, Jeannette Wild, Sean Davis, Wolfgang Steinbrecht, Thierry Leblanc, Richard Querel, Kleareti Tourpali, Robert Damadeo, Eliane Maillard Barras, René Stübi, Corinne Vigouroux, Carlo Arosio, Gerald Nedoluha, Ian Boyd, Roeland Van Malderen, Daan Hubert, Emmanuel Mahieu, Dan Smale, Ralf Sussmann

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRange (aeronautics)LatitudeEnvironmental scienceAtmospheric sciencesRegression analysisRegressionDistribution (mathematics)MeteorologyLotusStatisticsGeographyMathematicsGeodesyGeologyBiologyEngineeringEcology

Abstract

fetched live from OpenAlex

Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model". Information about and the most recent versions of each dataset can be found at their individual source locations: Merged satellite datasets SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA) SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA). GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA) SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA). SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany). SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada). Ground-based records: Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA) ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: Hohenpeissenberg: DWD, Germany Payerne:MeteoSwiss, Switzerland OHP, CNRS, France Hilo, NOAA, USA Lauder, NIWA, New Zealand lidar: http://www.ndacc.org/ . Measurement at the various stations are provided by the following institutions: Hohenpeissenberg: DWD, Germany OHP: CNRS, France MLO: JPL, NASA, USA Lauder: NIWA, New Zealand FTIR spectrometers – http://www.ndacc.org/ Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in http://www.ndacc.org/ must be used. Measurement used in the article are provided by the following institutions: Zugspitze: KIT, Germany Jungfraujoch: ULiège, GIRPAS team, Belgium Lauder: NIWA, New Zealand Microwave spectrometers: http://www.ndacc.org/ Measurement at the various stations are provided by the following institutions: Payerne: MeteoSwiss, Switzerland Mauna Loa: NRL, USA Lauder: NRL, USA Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.022

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 head0.053
GPT teacher head0.254
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicAtmospheric Ozone and ClimateFrench-language works237,207