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Record W2886248597 · doi:10.1175/bams-d-17-0175.1

Overview of the NOAA/ESRL Federated Aerosol Network

2018· article· en· W2886248597 on OpenAlexaff
Elisabeth Andrews, Patrick J. Sheridan, J. A. Ogren, Derek Hageman, Anne Jefferson, Jim Wendell, Andrés Alástuey, Lucas Alados‐Arboledas, Michael Bergin, Marina Ealo, A. Gannet Hallar, András Hoffer, Ivo Kalapov, Melita Keywood, Jeong-Eun Kim, Sang‐Woo Kim, Felicia Kolonjari, Casper Labuschagne, Neng‐Huei Lin, A. M. Macdonald, O. L. Mayol‐Bracero, Ian B. McCubbin, Marco Pandolfi, Fabienne Reisen, Sangeeta Sharma, James P. Sherman, M. Sorribas, Junying Sun

Bibliographic record

VenueBulletin of the American Meteorological Society · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsAerosolMeteorologyEnvironmental scienceRadiative forcingAtmosphere (unit)Geography

Abstract

fetched live from OpenAlex

Abstract To estimate global aerosol radiative forcing, measurements of aerosol optical properties are made by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)’s Global Monitoring Division (GMD) and their collaborators at 30 monitoring locations around the world. Many of the sites are located in regions influenced by specific aerosol types (Asian and Saharan desert dust, Asian pollution, biomass burning, etc.). This network of monitoring stations is a shared endeavor of NOAA and many collaborating organizations, including the World Meteorological Organization (WMO)’s Global Atmosphere Watch (GAW) program, the U.S. Department of Energy (DOE), several U.S. and foreign universities, and foreign science organizations. The result is a long-term cooperative program making atmospheric measurements that are directly comparable with those from all the other network stations and with shared data access. The protocols and software developed to support the program facilitate participation in GAW’s atmospheric observation strategy, and the sites in the NOAA/ESRL network make up a substantial subset of the GAW aerosol observations. This paper describes the history of the NOAA/ESRL Federated Aerosol Network, details about measurements and operations, and some recent findings from the network measurements.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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.

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

Citations112
Published2018
Admission routes1
Has abstractyes

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