Air pollution success stories in the United States: The value of long-term observations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We summarize past examples of the use of science to document the effectiveness of policy in air quality management. Our goal is to inform public discourse amidst attempts to negate the relevance and value of scientific data and fact-based analysis in favor of partisan opinion and ideology. Although air quality is fundamental to environmental and human health, air pollution has degraded natural systems and reduced economic and cultural benefits and services. The quality of air and fresh water across much of the United States vastly improved in recent decades in response to the Clean Air and Clean Water Acts and other rules and policies. We point to recently observed decreases in air pollution and its effects attributable to policy that have been informed by environmental monitoring and research. Examples include decreased environmental lead contamination due to the elimination of tetraethyl lead from gasoline, decreases in tropospheric ozone, improved visibility from reduced airborne particulate matter, declines in atmospheric sulfur and nitrogen deposition that acidify the environment and declines in atmospheric mercury and subsequent bioaccumulation of toxic methyl mercury. Pollutant reductions have provided environmental, social, and economic benefits, highlighting the urgency to apply these lessons to address current critical environmental issues such as emissions of greenhouse gases. These examples underscore the important role of data from long-term research and monitoring as part of fact-based decision-making in environmental policy.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it