Equitable cleanup of Superfund sites leaving no U.S. community behind
Bibliographic record
Abstract
Superfund sites are recognized as the most contaminated locations across the U.S. Here we introduce two metrics: (i) the disparity percentage, which quantifies the overrepresentation of vulnerable populations in proximity to Superfund sites, and (ii) the Superfund exposure score, which evaluates the population proportion in a geographical region affected by such proximity. We employ the metrics to develop an Action Priority Matrix (APM) categorizing states and regions into four tiers of cleanup priorities, informed by a spatial analysis of 13,453 Superfund sites across the U.S. About 80% of the U.S. population live within 10 km of at least one Superfund site, with nearly 60% of them residing in areas lacking any cleanup efforts. Asian, Black, and disadvantaged populations are found to be disproportionately overrepresented in Superfund host block groups. Seven states are identified for urgent cleanups using the proposed APM, providing a systematic approach to equitable resource allocation for cleanups.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".