Overview of the NOAA/ESRL Federated Aerosol Network
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".