From Raw Data to Television: How the United States Environmental Protection Agency’s AIRNow System Works
Bibliographic record
Abstract
The United States (U.S.) Environmental Protection Agency’s (EPA’s) AIRNow program provides easy access to air quality information through the Internet (www.epa.gov/airnow). Real-time air quality data (ground-level ozone) are collected from over 1200 monitors across the U.S. and Canada. The data are processed and quality-controlled every hour at the Data Management Center (DMC) where ozone maps are produced. These maps show hourly formation and movement of ground-level ozone and are vividly colored to correspond to the Air Quality Index (AQI). Furthermore, files are transferred to various private weather service providers (WSPs) who, in turn, disseminate the data to the media (television, print, and Internet). In addition to ozone maps, the AIRNow web site collects and displays air quality forecasts provided by state and local air agencies for over 265 U.S. cities. These ozone maps and forecasts enable state and local governments to inform the public of possible health impacts and voluntary emission reduction programs. The AIRNow program is dynamic and currently expanding to include PM2.5 data, forecasts, and mapping products that will be publicly available in 2003.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.047 |
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".