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Record W4393835768 · doi:10.5281/zenodo.3981434

Dataset: Global assessment of precipitation chemistry and deposition

2020· dataset· en· W4393835768 on OpenAlexaffabout
Robert Vet, Richard S. Artz, Silvina Carou, Mike Shaw, Chul‐Un Ro, Wenche Aas, Alex R. Baker, Van C. Bowersox, Frank Dentener, Corinne Galy‐Lacaux, Amy Hou, Jacobus J. Pienaar, Robert Gillett, M. C. Forti, Sergey A. Gromov, Hiroshi Hara, Т. В. Ходжер, N. M. Mahowald, Slobodan Ničković, P.S.P. Rao, Neville W. Reid

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsMinistry of the Environment, Conservation and ParksEnvironment and Climate Change Canada
Fundersnot available
KeywordsPrecipitationDeposition (geology)ChemistryEnvironmental chemistryEnvironmental scienceMeteorologyGeologyGeographyPaleontology

Abstract

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An international team of 21 scientists from 14 countries, working under the auspices of the WMO Global Atmosphere Watch Scientific Advisory Group for Precipitation Chemistry, has produced a global assessment of precipitation chemistry and deposition. This assessment appears in a Special Issue of the journal, Atmospheric Environment, Volume 93 (2014), and includes three articles: Preface by Guest Editors, Robert Vet (Environment Canada), Richard Artz (National Oceanic and Atmospheric Administration), and Silvina Carou (Environment Canada). http://dx.doi.org/10.1016/j.atmosenv.2013.11.013. Robert Vet, Richard S. Artz, Silvina Carou, Mike Shaw, Chul-Un Ro, Wenche Aas, Alex Baker, Van C. Bowersox, Frank Dentener, Corinne Galy-Lacaux, Amy Hou, Jacobus J. Pienaar, Robert Gillett, M. Cristina Forti, Sergey Gromov, Hiroshi Hara, Tamara Khodzer, Natalie M. Mahowald, Slobodan Nickovic, P.S.P. Rao, and Neville W. Reid. A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. http://dx.doi.org/10.1016/j.atmosenv.2013.10.060. Addendum by Vet, et al. http://dx.doi.org/10.1016/j.atmosenv.2014.02.017. The goal of the assessment was to provide the international science and policy communities with the best available data and information on regionally-representative precipitation chemistry and atmospheric deposition. The information in this publication, together with the supporting data and maps, is an important contribution to the study of atmospheric deposition and to related scientific studies, such as the study of ecosystem impacts, human health effects, nutrient processing, climate change, global and hemispheric modeling, and biogeochemical cycling. Data used in the assessment included best-available estimates of precipitation concentrations and wet, dry, and total deposition of major ions, sea salt, and phosphorus in North America, South America, Europe, Africa, Asia, Oceania, and the oceans for two periods, 2000-2002 and 2005-2007. Due to the limited contemporary data for phosphorus and organic acids, it was necessary to extend the study period back to the mid-1990s for these species. In order to fill gaps in the geographic coverage of the measurements, 2000-2002 data were combined with 2001 ensemble-mean results from 21 global chemical transport models. The model results were produced during Phase I of the Coordinated Model Studies Activities of the Task Force on Hemispheric Transport of Air Pollution (Dentener, et al. 2006. Global Biogeochem. Cycles 20, 21. http://dx.doi.org/10.1029/2005GB002672. Maps of major ions in precipitation and deposition were generated from the combined measurement and model results. A major product of the assessment was the preparation of data sets of quality-assured ion concentrations and wet deposition, dry deposition estimates, and model results. Use and publication of the global assessment data sets for scientific, policy-related, or educational purposes are encouraged. Please use the following citation to identify the data set and its source: Vet, R., R.S. Artz, S. Carou, M. Shaw, C.-U. Ro, W. Aas, A. Baker, V.C. Bowersox, F. Dentener, C. Galy-Lacaux, A. Hou, J.J. Pienaar, R. Gillett, M.C. Forti, S. Gromov, H. Hara, T. Khodzher, N.M. Mahowald, S. Nickovic, P.S.P. Rao, N.W. Reid. 2019. Data associated with the following publication: Vet et al. (2014). A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. Atmospheric Environment, 93, 3-100, August 2014, doi.org/10.1016/j.atmosenv.2013.10.060. Enter data file name(s) accessed from the World Data Centre for Precipitation Chemistry. Please also include the following acknowledgment in publications: The authors gratefully acknowledge the sources of precipitation chemistry and deposition data acknowledged on page 92 of Vet et al. (2014) Atmospheric Environment, 93, http://dx.doi.org/10.1016/j.atmosenv.2013.10.060.

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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.010

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 head0.033
GPT teacher head0.287
Teacher spread0.253 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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