Network analysis of ground-level ozone: Implications for environmental policy and air quality management
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
As network science emerges as a transformative tool in the ’Big Science’ era, this study harnesses this tool to model ground-level ozone distribution dynamics across US states under different regulatory frameworks from 1980 to 2017. The evolution of these regulations provides a unique natural experiment to analyze how network-driven models evolve amidst varied environmental policies. By constructing a network from ozone monitoring sites connected based on Pearson correlation coefficients, we analyzed the structural evolution of air quality networks. Techniques like community detection highlighted localized and temporal variations in ozone levels, influenced by meteorological and energy consumption data. Our findings reveal that geographical and regulatory factors significantly shape the network structure. This research demonstrates how network science can elucidate the complex interdependencies in environmental systems and suggests that integrating these insights could refine air quality regulations, promoting more effective management strategies in line with advanced environmental modeling needs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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